%%% -*-BibTeX-*-
%%% ====================================================================
%%%  BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.61",
%%%     date            = "16 March 2014",
%%%     time            = "10:22:22 MST",
%%%     filename        = "pagerank.bib",
%%%     address         = "University of Utah
%%%                        Department of Mathematics, 110 LCB
%%%                        155 S 1400 E RM 233
%%%                        Salt Lake City, UT 84112-0090
%%%                        USA",
%%%     telephone       = "+1 801 581 5254",
%%%     FAX             = "+1 801 581 4148",
%%%     URL             = "http://www.math.utah.edu/~beebe",
%%%     checksum        = "11545 13086 63263 626768",
%%%     email           = "beebe at math.utah.edu, beebe at acm.org,
%%%                        beebe at computer.org (Internet)",
%%%     codetable       = "ISO/ASCII",
%%%     keywords        = "LambdaRank; PageRank; Web information
%%%                        retrieval; Web search",
%%%     license         = "public domain",
%%%     supported       = "yes",
%%%     docstring       = "This is a bibliography of publications
%%%                        about the Google Brin/Page PageRank
%%%                        algorithm, and its historical background.
%%%                        The algorithm is at the core of text
%%%                        searching done by Google and other
%%%                        Web-indexing companies.
%%%
%%%                        At version 1.61, the year coverage looked
%%%                        like this:
%%%
%%%                             1941 (   1)    1966 (   0)    1991 (   0)
%%%                             1947 (   0)    1972 (   0)    1997 (   1)
%%%                             1948 (   0)    1973 (   0)    1998 (   2)
%%%                             1951 (   0)    1976 (   0)    2001 (   7)
%%%                             1952 (   0)    1977 (   0)    2002 (  12)
%%%                             1953 (   0)    1978 (   0)    2003 (  23)
%%%                             1954 (   0)    1979 (   0)    2004 (  27)
%%%                             1955 (   0)    1980 (   0)    2005 (  50)
%%%                             1956 (   0)    1981 (   0)    2006 (  70)
%%%                             1957 (   0)    1982 (   0)    2007 (  90)
%%%                             1958 (   0)    1983 (   0)    2008 (  57)
%%%                             1959 (   0)    1984 (   0)    2009 (  44)
%%%                             1960 (   0)    1985 (   0)    2010 (  27)
%%%                             1961 (   0)    1986 (   0)    2011 (  18)
%%%                             1962 (   0)    1987 (   0)    2012 (  11)
%%%                             1963 (   0)    1988 (   0)    2013 (   5)
%%%                             1964 (   0)    1989 (   0)    2014 (   1)
%%%
%%%                             Article:        124
%%%                             Book:            14
%%%                             InBook:           5
%%%                             InCollection:     3
%%%                             InProceedings:  205
%%%                             MastersThesis:    1
%%%                             Misc:             4
%%%                             PhdThesis:        2
%%%                             Proceedings:     81
%%%                             TechReport:       7
%%%
%%%                             Total entries:  446
%%%
%%%                        The checksum field above contains a CRC-16
%%%                        checksum as the first value, followed by the
%%%                        equivalent of the standard UNIX wc (word
%%%                        count) utility output of lines, words, and
%%%                        characters.  This is produced by Robert
%%%                        Solovay's checksum utility.",
%%%  }
%%% ====================================================================

%%% ====================================================================
%%% Institution abbreviations:

@String{inst-MATHWORKS          = "The MathWorks, Inc."}
@String{inst-MATHWORKS:adr      = "3 Apple Hill Drive, Natick, MA 01760-2098,
                                  USA"}

%%% ====================================================================
%%% Journal abbreviations:

@String{j-AMER-MATH-MONTHLY     = "American Mathematical Monthly"}

@String{j-APPL-MATH-COMP        = "Applied Mathematics and Computation"}

@String{j-APPL-MATH-LETT        = "Applied Mathematics Letters"}

@String{j-BIT                   = "BIT (Nordisk tidskrift for
                                  informationsbehandling)"}

@String{j-CACM                  = "Communications of the ACM"}

@String{j-COMP-J                = "The Computer Journal"}

@String{j-COMP-MATH-APPL        = "Computers and Mathematics with
                                  Applications"}

@String{j-COMP-NET-ISDN         = "Computer Networks and ISDN Systems"}

@String{j-COMP-NET-AMSTERDAM    = "Computer Networks (Amsterdam, Netherlands:
                                  1999)"}

@String{j-COMP-SURV             = "ACM Computing Surveys"}

@String{j-C-R-MATH-ACAD-SCI-PARIS = "Comptes Rendus Math{\'e}matique.
                                  Acad{\'e}mie des Sciences. Paris"}

@String{j-DOKL-AKAD-NAUK        = "Doklady Akademii nauk SSSR"}

@String{j-DYN-CONTIN-DISCR-IMPULS-B = "Dynamics of Continuous, Discrete \&
                                  Impulsive Systems. Series B. Applications \&
                                  Algorithms"}

@String{j-ELECTRON-TRANS-NUMER-ANAL = "Electronic Transactions on Numerical
                                Analysis (ETNA)"}

@String{j-IEEE-INTERNET-COMPUT  = "IEEE Internet Computing"}

@String{j-IEEE-TRANS-AUTOMAT-CONTR = "IEEE Transactions on Automatic Control"}

@String{j-IEEE-TRANS-KNOWL-DATA-ENG = "IEEE Transactions on Knowledge and Data
                                  Engineering"}

@String{j-IEEE-TRANS-PAR-DIST-SYS = "IEEE Transactions on Parallel and
                                    Distributed Systems"}

@String{j-IEEE-TRANS-PATT-ANAL-MACH-INTEL = "IEEE Transactions on Pattern
                                  Analysis and Machine Intelligence"}

@String{j-INF-RETR              = "Information Retrieval"}

@String{j-INFO-PROC-LETT        = "Information Processing Letters"}

@String{j-INFORM-THEOR-APPL     = "Informatique th{\'e}orique et applications :=
                                  Theoretical informatics and applications"}

@String{j-INT-J-BIFURC-CHAOS-APPL-SCI-ENG = "International journal of
                                  bifurcation and chaos in applied sciences
                                  and engineering"}

@String{j-INT-J-COMP-PROC-ORIENTAL-LANG = "International Journal of Computer
                                  Processing of Oriental Languages (IJCPOL)"}

@String{j-INTERNET-MATH         = "Internet Mathematics"}

@String{j-J-ACM                 = "Journal of the ACM"}

@String{j-J-COMP-APPL-MATH      = "Journal of Computational and Applied
                                  Mathematics"}

@String{j-J-GRID-COMP           = "Journal of Grid Computing"}

@String{j-J-SCI-COMPUT          = "Journal of Scientific Computing"}

@String{j-LECT-NOTES-COMP-SCI   = "Lecture Notes in Computer Science"}

@String{j-LINEAR-ALGEBRA-APPL   = "Linear Algebra and its Applications"}

@String{j-MATH-COMPUT           = "Mathematics of Computation"}

@String{j-MATH-INTEL            = "The Mathematical Intelligencer"}

@String{j-NUM-LIN-ALG-APPL      = "Numerical Linear Algebra with Applications"}

@String{j-PHYS-TODAY            = "Physics Today"}

@String{j-PROC-NATL-ACAD-SCI-USA = "Proceedings of the National Academy of
                                  Sciences of the United States of America"}

@String{j-PROC-VLDB-ENDOWMENT   = "Proceedings of the VLDB Endowment"}

@String{j-SCIENTOMETRICS        = "Scientometrics"}

@String{j-SIAM-J-MAT-ANA-APPL   = "SIAM Journal on Matrix Analysis and
                                  Applications"}

@String{j-SIAM-J-NUMER-ANAL     = "SIAM Journal on Numerical Analysis"}

@String{j-SIAM-J-SCI-COMP       = "SIAM Journal on Scientific Computing"}

@String{j-SIAM-REVIEW           = "SIAM Review"}

@String{j-SIGMETRICS            = "ACM SIGMETRICS Performance Evaluation
                                  Review"}

@String{j-STOCH-MODELS          = "Stochastic Models"}

@String{j-THEOR-INFORM-APPL     = "Theoretical Informatics and Applications.
                                  Informatique Th{\'e}orique et Applications"}

@String{j-TIST                 = "ACM Transactions on Intelligent Systems and
                                  Technology (TIST)"}

@String{j-TKDD                  = "ACM Transactions on Knowledge
                                  Discovery from Data (TKDD)"}

@String{j-TMIS                  = "ACM Transactions on Management Information
                                  Systems (TMIS)"}

@String{j-TODS                  = "ACM Transactions on Database Systems"}

@String{j-TOIS                  = "ACM Transactions on Information Systems"}

@String{j-TOIT                  = "ACM Transactions on Internet Technology
                                  (TOIT)"}

@String{j-TOMCCAP               = "ACM Transactions on Multimedia Computing,
                                  Communications, and Applications"}

@String{j-TOSEM                 = "ACM Transactions on Software Engineering and
                                   Methodology"}

@String{j-TRETS                 = "ACM Transactions on Reconfigurable Technology
                                  and Systems (TRETS)"}

@String{j-TWEB                  = "ACM Transactions on the Web (TWEB)"}

@String{j-VLDB-J                = "VLDB Journal: Very Large Data Bases"}

%%% ====================================================================
%%% Publisher abbreviations:

@String{pub-ACM                 = "ACM Press"}
@String{pub-ACM:adr             = "New York, NY 10036, USA"}

@String{pub-ELSEVIER            = "Elsevier"}
@String{pub-ELSEVIER:adr        = "Amsterdam, The Netherlands"}

@String{pub-HARVARD             = "Harvard University Press"}
@String{pub-HARVARD:adr         = "Cambridge, MA, USA"}

@String{pub-IEEE                = "IEEE Computer Society Press"}
@String{pub-IEEE:adr            = "1109 Spring Street, Suite 300,
                                  Silver Spring, MD 20910, USA"}

@String{pub-IOS                 = "IOS Press"}
@String{pub-IOS:adr             = "Amsterdam, The Netherlands"}

@String{pub-MORGAN-KAUFMANN     = "Morgan Kaufmann Publishers"}
@String{pub-MORGAN-KAUFMANN:adr = "Los Altos, CA 94022, USA"}

@String{pub-PRINCETON           = "Princeton University Press"}
@String{pub-PRINCETON:adr       = "Princeton, NJ, USA"}

@String{pub-QUE                 = "Que Corporation"}
@String{pub-QUE:adr             = "Indianapolis, IN, USA"}

@String{pub-SIAM                = "Society for Industrial and Applied
                                  Mathematics"}
@String{pub-SIAM:adr            = "Philadelphia, PA, USA"}

@String{pub-SV                  = "Springer-Verlag"}
@String{pub-SV:adr              = "Berlin, Germany~/ Heidelberg, Germany~/
                                  London, UK~/ etc."}

@String{pub-WILEY               = "Wiley"}
@String{pub-WILEY:adr           = "New York, NY, USA"}

%%% ====================================================================
%%% Series abbreviations:

@String{ser-LNAI                = "Lecture Notes in Artificial Intelligence"}

@String{ser-LNCIS               = "Lecture Notes in Control and Information
                                  Science"}

@String{ser-LNCS                = "Lecture Notes in Computer Science"}

%%% ====================================================================
%%% Bibliography entries, sorted by year, and within years, by citation
%%% label, using ``bibsort -byyear''.

@Book{Leontief:1941:SAE,
  author =       "Wassily W. Leontief",
  title =        "The Structure of {American} Economy, 1919--1929: an
                 empirical application of equilibrium analysis",
  publisher =    pub-HARVARD,
  address =      pub-HARVARD:adr,
  pages =        "xi + 181",
  year =         "1941",
  LCCN =         "????",
  bibdate =      "Fri Feb 19 15:19:39 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://nobelprize.org/nobel_prizes/economics/laureates/1973/",
  acknowledgement = ack-nhfb,
  remark =       "The author was awarded the Nobel Prize in Economics in
                 1973. Franceschet \cite{Franceschet:2010:PSS} traces
                 the PageRank algorithm back to this book.",
}

@Article{Marchiori:1997:QCI,
  author =       "Massimo Marchiori",
  title =        "The quest for correct information on the {Web}: Hyper
                 search engines",
  journal =      j-COMP-NET-ISDN,
  volume =       "29",
  number =       "8--13",
  pages =        "1225--1236",
  day =          "30",
  month =        sep,
  year =         "1997",
  CODEN =        "CNISE9",
  ISSN =         "0169-7552 (print), 1879-2324 (electronic)",
  ISSN-L =       "0169-7552",
  bibdate =      "Fri Sep 24 20:21:54 MDT 1999",
  bibsource =    "http://www.elsevier.com/cgi-bin/cas/tree/store/cna/cas_free/browse/browse.cgi?year=1997&volume=29&issue=08-13;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.computerworld.com/s/article/9222018/Italian_mathematician_prepares_to_challenge_Google;
                 http://www.elsevier.com/cgi-bin/cas/tree/store/comnet/cas_sub/browse/browse.cgi?year=1997&volume=29&issue=08-13&aid=1711",
  acknowledgement = ack-nhfb,
  fjournal =     "Computer Networks and ISDN Systems",
  journal-URL =  "http://www.sciencedirect.com/science/journal/01697552",
  remark =       "This article is claimed in a 2011-11-21 ComputerWorld
                 story to be a precursor of the Google PageRank
                 algorithm, although it refers to it as a 1996
                 conference article.",
}

@Article{Brin:1998:ALS,
  author =       "Sergey Brin and Lawrence Page",
  title =        "The anatomy of a large-scale hypertextual {Web} search
                 engine",
  journal =      j-COMP-NET-ISDN,
  volume =       "30",
  number =       "1--7",
  pages =        "107--117",
  day =          "1",
  month =        apr,
  year =         "1998",
  CODEN =        "CNISE9",
  ISSN =         "0169-7552 (print), 1879-2324 (electronic)",
  ISSN-L =       "0169-7552",
  bibdate =      "Fri Sep 24 20:22:05 MDT 1999",
  bibsource =    "http://www.elsevier.com/cgi-bin/cas/tree/store/cna/cas_free/browse/browse.cgi?year=1998&volume=30&issue=1-7;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.elsevier.com/cas/tree/store/comnet/sub/1998/30/1-7/1921.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "Computer Networks and ISDN Systems",
  journal-URL =  "http://www.sciencedirect.com/science/journal/01697552",
}

@TechReport{Page:1998:PCR,
  author =       "Lawrence Page and Sergey Brin and Rajeev Motwani and
                 Terry Winograd",
  title =        "The {PageRank} Citation Ranking: Bringing Order to the
                 Web",
  institution =  "Stanford Digital Library Technologies Project,
                 Stanford University",
  address =      "Stanford, CA, USA",
  pages =        "17",
  day =          "11",
  month =        nov,
  year =         "1998",
  bibdate =      "Thu Oct 24 15:13:54 2002",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/master.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://dbpubs.stanford.edu/pub/1999-66;
                 http://ilpubs.Stanford.edu:8090/422/",
  abstract =     "The importance of a Web page is an inherently
                 subjective matter, which depends on the readers
                 interests, knowledge and attitudes. But there is still
                 much that can be said objectively about the relative
                 importance of Web pages. This paper describes PageRank,
                 a mathod for rating Web pages objectively and
                 mechanically, effectively measuring the human interest
                 and attention devoted to them. We compare PageRank to
                 an idealized random Web surfer. We show how to
                 efficiently compute PageRank for large numbers of
                 pages. And, we show how to apply PageRank to search and
                 to user navigation.",
  acknowledgement = ack-nhfb,
  annote =       "This is the Google search algorithm.",
}

@InProceedings{Finkelstein:2001:PSC,
  author =       "Lev Finkelstein and Evgeniy Gabrilovich and Yossi
                 Matias and Ehud Rivlin and Zach Solan and Gadi Wolfman
                 and Eytan Ruppin",
  title =        "Placing search in context: the concept revisited",
  crossref =     "ACM:2001:CPT",
  pages =        "406--414",
  year =         "2001",
  DOI =          "http://doi.acm.org/10.1145/371920.372094",
  bibdate =      "Mon May 10 14:07:25 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Kruschwitz:2001:WKD,
  author =       "U. Kruschwitz",
  booktitle =    "{IEEE International Conference on Systems, Man, and
                 Cybernetics, 2001, 7--10 October, 2001, Tucson, AZ}",
  title =        "World knowledge for the domain of your choice",
  crossref =     "Bahill:2001:IIC",
  pages =        "555--560",
  year =         "2001",
  DOI =          "http://dx.doi.org/10.1109/ICSMC.2001.969872",
  bibdate =      "Thu May 06 13:31:24 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Modern Web search engines access large parts of the
                 publicly indexable Web. Relevant sites can be found
                 easily thanks to advanced techniques such as Google's
                 PageRank algorithm. However, a common problem remains
                 the large number of matching documents being returned
                 even for fairly specific queries. The same problem can
                 be observed in domains that are more limited like
                 intranets or local Web sites. By enriching a search
                 engine with knowledge about the domain one could
                 provide much more feedback for a query than just a list
                 of matches, such as a number of useful discriminating
                 terms, that would allow the user to constrain the
                 query. We present a way of building such a domain model
                 automatically by analyzing the markup of the source
                 data. We will illustrate this with some examples taken
                 from our sample domain.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Ng:2001:SAL,
  author =       "Andrew Y. Ng and Alice X. Zheng and Michael I.
                 Jordan",
  title =        "Stable algorithms for link analysis",
  crossref =     "Croft:2001:PAI",
  pages =        "258--266",
  year =         "2001",
  DOI =          "http://dx.doi.org/10.1145/383952.384003",
  bibdate =      "Wed Jun 01 18:24:42 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The Kleinberg HITS and the Google PageRank algorithms
                 are eigenvector methods for identifying
                 ``authoritative'' or ``influential'' articles, given
                 hyperlink or citation information. That such algorithms
                 should give reliable or consistent answers is surely a
                 desideratum, and in \cite{ijcaiPaper}, we analyzed when
                 they can be expected to give stable rankings under
                 small perturbations to the linkage patterns. In this
                 paper, we extend the analysis and show how it gives
                 insight into ways of designing stable link analysis
                 methods. This in turn motivates two new algorithms,
                 whose performance we study empirically using citation
                 data and web hyperlink data.",
  acknowledgement = ack-nhfb,
}

@Misc{Page:2001:MNR,
  author =       "Lawrence Page",
  title =        "Method for node ranking in a linked database",
  howpublished = "US Patent 6,285,999",
  day =          "4",
  month =        sep,
  year =         "2001",
  bibdate =      "Thu Jun 02 08:24:11 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "Filed January 9, 1998. Expires around January 9,
                 2018.",
  URL =          "http://patft.uspto.gov/netahtml/PTO/srchnum.htm",
  abstract =     "A method assigns importance ranks to nodes in a linked
                 database, such as any database of documents containing
                 citations, the world wide web or any other hypermedia
                 database. The rank assigned to a document is calculated
                 from the ranks of documents citing it. In addition, the
                 rank of a document is calculated from a constant
                 representing the probability that a browser through the
                 database will randomly jump to the document. The method
                 is particularly useful in enhancing the performance of
                 search engine results for hypermedia databases, such as
                 the world wide web, whose documents have a large
                 variation in quality.",
  acknowledgement = ack-nhfb,
  remark =       "This may be the main patent behind the Google search
                 engine.",
}

@InProceedings{Arasu:2002:PCS,
  author =       "Arvind Arasu and Jasmine Novak and Andrew Tomkins and
                 John Tomlin",
  title =        "{PageRank} Computation and the Structure of the {Web}:
                 Experiments and Algorithms",
  crossref =     "Anonymous:2002:PIW",
  pages =        "??--??",
  year =         "2002",
  bibdate =      "Thu Oct 24 15:18:39 2002",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www2002.org/CDROM/",
  URL =          "http://www.math.utah.edu/pub/tex/bib/master.bib;
                 http://www2002.org/CDROM/poster/173.pdf",
  acknowledgement = ack-nhfb,
  annote =       "PageRank is the Google search algorithm.",
  pagecount =    "5",
}

@InProceedings{Chen:2002:ETC,
  author =       "Yen-Yu Chen and Qingqing Gan and Torsten Suel",
  editor =       "{ACM}",
  booktitle =    "Conference on Information and Knowledge Management
                 Proceedings of the eleventh international conference on
                 Information and knowledge management",
  title =        "{I/O}-efficient techniques for computing {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "549--557",
  year =         "2002",
  DOI =          "http://dx.doi.org/10.1145/238386.238450",
  ISBN =         "1-58113-492-4",
  ISBN-13 =      "978-1-58113-492-6",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Over the last few years, most major search engines
                 have integrated link-based ranking techniques in order
                 to provide more accurate search results. One widely
                 known approach is the Pagerank technique, which forms
                 the basis of the Google ranking scheme, and which
                 assigns a global importance measure to each page based
                 on the importance of other pages pointing to it. The
                 main advantage of the Pagerank measure is that it is
                 independent of the query posed by a user; this means
                 that it can be precomputed and then used to optimize
                 the layout of the inverted index structure accordingly.
                 However, computing the Pagerank measure requires
                 implementing an iterative process on a massive graph
                 corresponding to billions of web pages and
                 hyperlinks.In this paper, we study I/O-efficient
                 techniques to perform this iterative computation. We
                 derive two algorithms for Pagerank based on techniques
                 proposed for out-of-core graph algorithms, and compare
                 them to two existing algorithms proposed by Haveliwala.
                 We also consider the implementation of a recently
                 proposed topic-sensitive version of Pagerank. Our
                 experimental results show that for very large data
                 sets, significant improvements over previous results
                 can be achieved on machines with moderate amounts of
                 memory. On the other hand, at most minor improvements
                 are possible on data sets that are only moderately
                 larger than memory, which is the case in many practical
                 scenarios.",
  acknowledgement = ack-nhfb,
  keywords =     "external memory algorithms; link-based ranking;
                 out-of-core; pagerank; search engines",
}

@InProceedings{Chen:2002:UFW,
  author =       "Zheng Chen and Li Tao and Jidong Wang and Liu Wenyin
                 and Wei-Ying Ma",
  title =        "A unified framework for {Web} link analysis",
  crossref =     "WangLing:2002:PTI",
  pages =        "63--70",
  year =         "2002",
  DOI =          "http://dx.doi.org/10.1109/WISE.2002.1181644",
  bibdate =      "Thu May 06 14:00:37 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@Article{Dhyani:2002:SWM,
  author =       "Devanshu Dhyani and Wee Keong Ng and Sourav S.
                 Bhowmick",
  title =        "A survey of {Web} metrics",
  journal =      j-COMP-SURV,
  volume =       "34",
  number =       "4",
  pages =        "469--503",
  month =        dec,
  year =         "2002",
  CODEN =        "CMSVAN",
  DOI =          "http://doi.acm.org/10.1145/592642.592645",
  ISSN =         "0360-0300 (print), 1557-7341 (electronic)",
  ISSN-L =       "0360-0300",
  bibdate =      "Thu Jun 19 10:18:33 MDT 2008",
  bibsource =    "http://www.acm.org/pubs/contents/journals/surveys/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/compsurv.bib",
  abstract =     "The unabated growth and increasing significance of the
                 World Wide Web has resulted in a flurry of research
                 activity to improve its capacity for serving
                 information more effectively. But at the heart of these
                 efforts lie implicit assumptions about `quality' and
                 `usefulness' of Web resources and services. This
                 observation points towards measurements and models that
                 quantify various attributes of web sites. The science
                 of measuring all aspects of information, especially its
                 storage and retrieval or informetrics has interested
                 information scientists for decades before the existence
                 of the Web. Is Web informetrics any different, or is it
                 just an application of classical informetrics to a new
                 medium? In this article, we examine this issue by
                 classifying and discussing a wide ranging set of Web
                 metrics. We present the origins, measurement functions,
                 formulations and comparisons of well-known Web metrics
                 for quantifying Web graph properties, Web page
                 significance, Web page similarity, search and
                 retrieval, usage characterization and information
                 theoretic properties. We also discuss how these metrics
                 can be applied for improving Web information access and
                 use.",
  acknowledgement = ack-nhfb,
  fjournal =     "ACM Computing Surveys",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J204",
  keywords =     "Information theoretic; PageRank; quality metrics; Web
                 graph; Web metrics; Web page similarity",
}

@InProceedings{Ding:2002:PHU,
  author =       "Chris Ding and Xiaofeng He and Parry Husbands and
                 Hongyuan Zha and Horst D. Simon",
  editor =       "{ACM}",
  booktitle =    "Annual ACM Conference on Research and Development in
                 Information Retrieval Proceedings of the 25th annual
                 international ACM SIGIR conference on Research and
                 development in information retrieval",
  title =        "PageRank, {HITS} and a unified framework for link
                 analysis",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "353--354",
  year =         "2002",
  DOI =          "http://dx.doi.org/10.1145/324133.324140",
  ISBN =         "1-58113-561-0",
  ISBN-13 =      "978-1-58113-561-9",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Two popular link-based webpage ranking algorithms are
                 (i) PageRank[1] and (ii) HITS (Hypertext Induced Topic
                 Selection)[3]. HITS makes the crucial distinction of
                 hubs and authorities and computes them in a mutually
                 reinforcing way. PageRank considers the hyperlink
                 weight normalization and the equilibrium distribution
                 of random surfers as the citation score. We generalize
                 and combine these key concepts into a unified
                 framework, in which we prove that rankings produced by
                 PageRank and HITS are both highly correlated with the
                 ranking by in-degree and out-degree.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Haveliwala:2002:TSP,
  author =       "Taher H. Haveliwala",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceedings of
                 the 11th international conference on World Wide Web",
  title =        "Topic-sensitive {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "517--526",
  year =         "2002",
  DOI =          "http://dx.doi.org/10.1145/511446.511513",
  ISBN =         "1-58113-449-5",
  ISBN-13 =      "978-1-58113-449-0",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In the original PageRank algorithm for improving the
                 ranking of search-query results, a single PageRank
                 vector is computed, using the link structure of the
                 Web, to capture the relative 'importance' of Web pages,
                 independent of any particular search query. To yield
                 more accurate search results, we propose computing a
                 set of PageRank vectors, biased using a set of
                 representative topics, to capture more accurately the
                 notion of importance with respect to a particular
                 topic. By using these (precomputed) biased PageRank
                 vectors to generate query-specific importance scores
                 for pages at query time, we show that we can generate
                 more accurate rankings than with a single, generic
                 PageRank vector. For ordinary keyword search queries,
                 we compute the topic-sensitive PageRank scores for
                 pages satisfying the query using the topic of the query
                 keywords. For searches done in context (e.g., when the
                 search query is performed by highlighting words in a
                 Web page), we compute the topic-sensitive PageRank
                 scores using the topic of the context in which the
                 query appeared.",
  acknowledgement = ack-nhfb,
  keywords =     "link structure; PageRank; personalized search; search;
                 search in context; web graph",
}

@Article{Kim:2002:ICP,
  author =       "Sung Jin Kim and Sang Ho Lee",
  title =        "An Improved Computation of the {PageRank} Algorithm",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "2291",
  pages =        "73--85",
  year =         "2002",
  CODEN =        "LNCSD9",
  ISBN =         "3-540-43343-0",
  ISBN-13 =      "978-3-540-43343-9",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/lncs2002a.bib;
                 http://link.springer-ny.com/link/service/series/0558/tocs/t2291.htm;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer-ny.com/link/service/series/0558/bibs/2291/22910073.htm;
                 http://link.springer-ny.com/link/service/series/0558/papers/2291/22910073.pdf",
  ZMnumber =     "1056.68526",
  acknowledgement = ack-nhfb,
  fjournal =     "Lecture Notes in Computer Science",
}

@TechReport{Moler:2002:CCW,
  author =       "Cleve B. Moler",
  title =        "{Cleve}'s Corner: The World's Largest Matrix
                 Computation: {Google}'s {PageRank} is an eigenvector of
                 a matrix of order $2.7$ billion",
  type =         "Technical note",
  institution =  inst-MATHWORKS,
  address =      inst-MATHWORKS:adr,
  pages =        "1",
  month =        oct,
  year =         "2002",
  bibdate =      "Thu Oct 24 07:16:21 2002",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/m/moler-cleve-b.bib;
                 http://www.math.utah.edu/pub/tex/bib/matlab.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.mathworks.com/company/newsletter/clevescorner/oct02_cleve.shtml",
  acknowledgement = ack-nhfb,
  keywords =     "Matlab",
}

@Article{Pandurangan:2002:UPC,
  author =       "Gopal Pandurangan and Prabhakar Raghavan and Eli
                 Upfal",
  title =        "Using {PageRank} to characterize {Web} structure",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "2387",
  pages =        "330--339",
  year =         "2002",
  CODEN =        "LNCSD9",
  DOI =          "http://dx.doi.org/10.1007/3-540-45655-4_36",
  ISBN =         "3-540-43996-X",
  ISBN-13 =      "978-3-540-43996-7",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  LCCN =         "????",
  MRclass =      "68M10 68U35",
  MRnumber =     "MR2064528",
  bibdate =      "Tue Sep 10 19:10:08 MDT 2002",
  bibsource =    "http://link.springer-ny.com/link/service/series/0558/tocs/t2387.htm;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  URL =          "http://link.springer-ny.com/link/service/series/0558/bibs/2387/23870330.htm;
                 http://link.springer-ny.com/link/service/series/0558/papers/2387/23870330.pdf",
  ZMnumber =     "1077.68527",
  acknowledgement = ack-nhfb,
  fjournal =     "Lecture Notes in Computer Science",
}

@Article{Pretto:2002:TAG,
  author =       "Luca Pretto",
  title =        "A Theoretical Analysis of {Google}'s {PageRank}",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "2476",
  pages =        "131--144",
  year =         "2002",
  CODEN =        "LNCSD9",
  ISBN =         "3-540-44158-1",
  ISBN-13 =      "978-3-540-44158-8",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  LCCN =         "????",
  bibdate =      "Sat Nov 30 20:57:37 MST 2002",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/lncs2002e.bib;
                 http://link.springer-ny.com/link/service/series/0558/tocs/t2476.htm;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.de/link/service/series/0558/bibs/2476/24760131.htm;
                 http://link.springer.de/link/service/series/0558/papers/2476/24760131.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "Lecture Notes in Computer Science",
}

@Book{Baldi:2003:MIW,
  author =       "Pierre Baldi and Paolo Frasconi and Padhraic Smyth",
  title =        "Modeling the {Internet} and the {Web}: probabilistic
                 methods and algorithms",
  publisher =    pub-WILEY,
  address =      pub-WILEY:adr,
  pages =        "xix + 285",
  year =         "2003",
  ISBN =         "0-470-86492-3 (e-book), 0-470-84906-1",
  ISBN-13 =      "978-0-470-86492-0 (e-book), 978-0-470-84906-4",
  LCCN =         "TK5105.875.I57 B35 2003eb",
  bibdate =      "Fri Jun 3 10:03:23 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  subject =      "Internet; Mathematical models; Telecommunication;
                 Traffic; World Wide Web; Cyberspace; Probabilities;
                 Computers; Web; General; Networking; Intranets and
                 Extranets",
  tableofcontents = "Mathematical Background \\
                 Probability and Learning from a Bayesian Perspective
                 \\
                 Parameter Estimation from Data \\
                 Basic principles \\
                 A simple die example \\
                 Mixture Models and the Expectation Maximization
                 Algorithm \\
                 Graphical Models \\
                 Bayesian networks \\
                 Belief propagation \\
                 Learning directed graphical models from data \\
                 Classification \\
                 Clustering \\
                 Power-Law Distributions \\
                 Scale-free properties (80/20 rule) \\
                 Applications to Languages: Zipf's and Heaps' Laws \\
                 Origin of power-law distributions and Fermi's model \\
                 Basic WWW Technologies \\
                 Web Documents \\
                 SGML and HTML \\
                 General structure of an HTML document \\
                 Links \\
                 Resource Identifiers: URI, URL, and URN \\
                 Protocols \\
                 Reference models and TCP/IP \\
                 The domain name system \\
                 The Hypertext Transfer Protocol \\
                 Programming examples \\
                 Log Files \\
                 Search Engines \\
                 Coverage \\
                 Basic crawling \\
                 Web Graphs \\
                 Internet and Web Graphs \\
                 Power-law size \\
                 Power-law connectivity \\
                 Small-world networks \\
                 Power law of PageRank \\
                 The bow-tie structure \\
                 Generative Models for the Web Graph and Other Networks
                 \\
                 Web page growth \\
                 Lattice perturbation models: between order and disorder
                 \\
                 Preferential attachment models, or the rich get richer
                 \\
                 Copy models \\
                 PageRank models \\
                 Applications \\
                 Distributed search algorithms \\
                 Subgraph patterns and communities \\
                 Robustness and vulnerability \\
                 Notes and Additional Technical References \\
                 Text Analysis \\
                 Indexing \\
                 Compression techniques \\
                 Lexical Processing \\
                 Tokenization",
}

@InProceedings{Bianchini:2003:PWC,
  author =       "M. Bianchini and M. Gori and F. Scarselli",
  title =        "{PageRank} and {Web} communities",
  crossref =     "Liu:2003:ISW",
  pages =        "365--371",
  year =         "2003",
  DOI =          "http://dx.doi.org/10.1109/WI.2003.1241217",
  bibdate =      "Fri Feb 19 18:30:00 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1241217",
  abstract =     "The definition of the ordering of the Web pages,
                 returned on a given query, is a crucial topic, which
                 gives rise to the notion of Web visibility. A
                 fundamental contribution towards the conception of
                 appropriate ordering criteria has been given by means
                 of the introduction of PageRank, which takes into
                 account only the hyper-linked structure of the Web,
                 regardless of the content of the pages. In this paper,
                 we introduce a circuit analysis which allows us to
                 understand the distribution of PageRank, and show some
                 basic results for understanding the way it migrates
                 amongst communities. In particular, we highlight some
                 topological properties which suggest methods for the
                 promotion of Web communities. These results confirm the
                 importance and the effectiveness of PageRank for
                 discovering relevant information but, at the same time,
                 point out its vulnerability to spamming.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Chirita:2003:FRH,
  author =       "P.-A. Chirita and D. Olmedilla and W. Nejdl",
  booktitle =    "{First Latin American Web Congress, 2003. LA-WEB 2003,
                 Santiago, Chile, November 10--12, 2003. Proceedings}",
  title =        "Finding related hubs and authorities",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "214--215",
  year =         "2003",
  ISBN =         "0-7695-2058-8",
  ISBN-13 =      "978-0-7695-2058-2",
  LCCN =         "????",
  bibdate =      "Mon May 10 12:22:33 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  bookpages =    "xii + 241",
  keywords =     "PageRank",
}

@InProceedings{Ding:2003:PHU,
  author =       "C. H. Q. Ding and X. He and P. Husbands and H. Zha and
                 H. D. Simon",
  title =        "{PageRank}: {HITS} and a unified framework for link
                 analysis",
  crossref =     "Barbara:2003:PTS",
  pages =        "353--354",
  year =         "2003",
  bibdate =      "Fri Feb 19 15:15:08 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@TechReport{Haveliwala:2003:ACA,
  author =       "Tamer Haveliwala and Sepandar Kamvar and Glen Jeh",
  title =        "An analytical comparison of approaches to
                 personalizing {PageRank}",
  type =         "Technical report",
  number =       "2003-32",
  institution =  "Stanford InfoLab, Stanford University",
  address =      "Stanford, CA, USA",
  pages =        "4",
  month =        jun,
  year =         "2003",
  bibdate =      "Tue Jul 20 16:03:24 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ilpubs.stanford.edu:8090/596/",
  abstract =     "PageRank, the popular link-analysis algorithm for
                 ranking web pages, assigns a query and user independent
                 estimate of ``importance'' to web pages. Query and user
                 sensitive extensions of PageRank, which use a basis set
                 of biased PageRank vectors, have been proposed in order
                 to personalize the ranking function in a tractable way.
                 We analytically compare three recent approaches to
                 personalizing PageRank and discuss the tradeoffs of
                 each one.",
  acknowledgement = ack-nhfb,
}

@Article{Haveliwala:2003:TSP,
  author =       "Taher H. Haveliwala",
  title =        "Topic-sensitive {PageRank}: a context-sensitive
                 ranking algorithm for {Web} search",
  journal =      j-IEEE-TRANS-KNOWL-DATA-ENG,
  volume =       "15",
  number =       "4",
  pages =        "784--796",
  month =        jul,
  year =         "2003",
  CODEN =        "ITKEEH",
  DOI =          "http://dx.doi.org/10.1109/TKDE.2003.1208999",
  ISSN =         "1041-4347",
  ISSN-L =       "1041-4347",
  bibdate =      "Sat May 8 18:33:11 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1208999",
  abstract =     "The original PageRank algorithm for improving the
                 ranking of search-query results computes a single
                 vector, using the link structure of the Web, to capture
                 the relative ``importance'' of Web pages, independent
                 of any particular search query. To yield more accurate
                 search results, we propose computing a set of PageRank
                 vectors, biased using a set of representative topics,
                 to capture more accurately the notion of importance
                 with respect to a particular topic. For ordinary
                 keyword search queries, we compute the topic-sensitive
                 PageRank scores for pages satisfying the query using
                 the topic of the query keywords. For searches done in
                 context (e.g., when the search query is performed by
                 highlighting words in a Web page), we compute the
                 topic-sensitive PageRank scores using the topic of the
                 context in which the query appeared. By using linear
                 combinations of these (precomputed) biased PageRank
                 vectors to generate context-specific importance scores
                 for pages at query time, we show that we can generate
                 more accurate rankings than with a single, generic
                 PageRank vector. We describe techniques for efficiently
                 implementing a large-scale search system based on the
                 topic-sensitive PageRank scheme.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=27216",
  fjournal =     "IEEE Transactions on Knowledge and Data Engineering",
  journal-URL =  "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69",
  keywords =     "link analysis; PageRank; personalized search; ranking
                 algorithm.; search in context; web graph; Web search",
}

@InProceedings{Jeh:2003:SPW,
  author =       "Glen Jeh and Jennifer Widom",
  title =        "Scaling personalized web search",
  crossref =     "Hencsey:2003:PTI",
  year =         "2003",
  DOI =          "http://doi.acm.org/10.1145/775152.775191",
  bibdate =      "Mon May 10 14:17:38 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Recent web search techniques augment traditional text
                 matching with a global notion of ``importance'' based
                 on the linkage structure of the web, such as in
                 Google's PageRank algorithm. For more refined searches,
                 this global notion of importance can be specialized to
                 create personalized views of importance--for example,
                 importance scores can be biased according to a
                 user-specified set of initially-interesting pages.
                 Computing and storing all possible personalized views
                 in advance is impractical, as is computing personalized
                 views at query time, since the computation of each view
                 requires an iterative computation over the web graph.
                 We present new graph-theoretical results, and a new
                 technique based on these results, that encode
                 personalized views as partial vectors. Partial vectors
                 are shared across multiple personalized views, and
                 their computation and storage costs scale well with the
                 number of views. Our approach enables incremental
                 computation, so that the construction of personalized
                 views from partial vectors is practical at query time.
                 We present efficient dynamic programming algorithms for
                 computing partial vectors, an algorithm for
                 constructing personalized views from partial vectors,
                 and experimental results demonstrating the
                 effectiveness and scalability of our techniques.",
  acknowledgement = ack-nhfb,
}

@TechReport{Kamvar:2003:EBS,
  author =       "Sepandar D. Kamvar and Taher H. Haveliwala and
                 Christopher D. Manning and Gene H. Golub",
  title =        "Exploiting the block structure of the {Web} for
                 computing {PageRank}",
  type =         "Technical Report",
  number =       "2003-17",
  institution =  "Stanford InfoLab, Stanford University",
  address =      "Stanford, CA, USA",
  pages =        "????",
  year =         "2003",
  bibdate =      "Fri Feb 19 15:17:26 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "BlockRank; PageRank",
}

@InProceedings{Kamvar:2003:EMA,
  author =       "Sepandar D. Kamvar and Taher H. Haveliwala and
                 Christopher D. Manning and Gene H. Golub",
  title =        "Extrapolation Methods for Accelerating {PageRank}
                 Computations",
  crossref =     "Hencsey:2003:PTI",
  pages =        "261--270",
  year =         "2003",
  DOI =          "http://doi.acm.org/10.1145/775152.775190",
  bibdate =      "Wed Nov 10 16:22:54 2004",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://dbpubs.stanford.edu:8090/pub/2003-16;
                 http://doi.acm.org/10.1145/775152.775190;
                 http://www.stanford.edu/~sdkamvar/papers/extrapolation.pdf",
  abstract =     "We present a novel algorithm for the fast computation
                 of PageRank, a hyperlink-based estimate of the
                 ``importance'' of Web pages. The original PageRank
                 algorithm uses the Power Method to compute successive
                 iterates that converge to the principal eigenvector of
                 the Markov matrix representing the Web link graph. The
                 algorithm presented here, called Quadratic
                 Extrapolation, accelerates the convergence of the Power
                 Method by periodically subtracting off estimates of the
                 nonprincipal eigenvectors from the current iterate of
                 the Power Method. In Quadratic Extrapolation, we take
                 advantage of the fact that the first eigenvalue of a
                 Markov matrix is known to be 1 to compute the
                 nonprincipal eigenvectors using successive iterates of
                 the Power Method. Empirically, we show that using
                 Quadratic Extrapolation speeds up PageRank computation
                 by 25--300\% on a Web graph of 80 million nodes, with
                 minimal overhead. Our contribution is useful to the
                 PageRank community and the numerical linear algebra
                 community in general, as it is a fast method for
                 determining the dominant eigenvector of a matrix that
                 is too large for standard fast methods to be
                 practical.",
  acknowledgement = ack-nhfb,
  keywords =     "eigenvector computation; link analysis; PageRank",
}

@Article{Kang:2003:IPN,
  author =       "In-Ho Kang and Eun-Jung Oh and Gil Chang Kim",
  title =        "Incremental {PageRanking} for Newly Crawled {Web}
                 Pages",
  journal =      j-INT-J-COMP-PROC-ORIENTAL-LANG,
  volume =       "16",
  number =       "1",
  pages =        "87--??",
  month =        mar,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4279",
  bibdate =      "Thu Jan 06 07:59:01 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijcpol/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/ijcpol.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Computer Processing of Oriental Languages (IJCPOL)",
}

@InProceedings{Narayan:2003:TCW,
  author =       "B. L. Narayan and C. A. Murthy and S. K. Pal",
  title =        "Topic continuity for {Web} document categorization and
                 ranking",
  crossref =     "Liu:2003:ISW",
  pages =        "310--315",
  year =         "2003",
  bibdate =      "Thu May 06 13:46:52 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Ohmukai:2003:PPC,
  author =       "I. Ohmukai and H. Takeda and M. Miki",
  title =        "A proposal of the person-centered approach for
                 personal task management",
  crossref =     "Helal:2003:SAI",
  pages =        "234--240",
  year =         "2003",
  bibdate =      "Thu May 06 13:51:30 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "This paper proposes a human-centered approach for
                 personal task management in which people can decide
                 management of their tasks according to their
                 environments, including their subjective and
                 multivalent judgement and human relationships. In our
                 approach task management is modeled as a
                 decision-making process on their own resources. The
                 human decision-making process consists of three types
                 of activity, i.e., the intelligence activity, design
                 activity, and choice activity. The proposed system
                 assists each activity by three sub-systems, i.e.,
                 visualizer, optimizer and recommender respectively. At
                 first, visualizer indicates the attributes associated
                 with each task such as deadline, subjective priority,
                 and workload, which are determined by the user. The
                 optimizer generates executable schedules from these
                 tasks using an active scheduler and multi-objective
                 genetic algorithm. Finally, the recommender evaluates
                 these alternatives using an analytic hierarchy process.
                 The system is also able to analyze the human
                 relationships of the user group using the PageRank
                 algorithm, and this result is utilized to improve the
                 performance of the task scheduler. We implement a
                 client/server system which uses mobile phones and
                 verify the function of the proposed system along the
                 lines of two scenarios.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Sankaralingam:2003:DPP,
  author =       "Karthikeyan Sankaralingam and Simha Sethumadhavan and
                 James C. Browne",
  title =        "Distributed {PageRank} for {P2P} systems",
  crossref =     "IEEE:2003:IIS",
  pages =        "58--68",
  year =         "2003",
  DOI =          "http://dx.doi.org/10.1109/HPDC.2003.1210016",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1210016",
  abstract =     "This paper defines and describes a fully distributed
                 implementation of Google's highly effective PageRank
                 algorithm, for 'peer to peer' (P2P) systems. The
                 implementation is based on chaotic (asynchronous)
                 iterative solution of linear systems. The P2P
                 implementation also enables incremental computation of
                 pageranks as new documents are entered into or deleted
                 from the network. Incremental update enables
                 continuously accurate pageranks whereas the currently
                 centralized web crawl and computation over Internet
                 documents requires several days. This suggests possible
                 applicability of the distributed algorithm to pagerank
                 computations as a replacement for the centralized web
                 crawler based implementation for Internet documents. A
                 complete solution of the distributed pagerank
                 computation for an inplace network converges rapidly
                 (1\% accuracy in 10 iterations) for large systems
                 although the time for an iteration may be long. The
                 incremental computation resulting from addition of a
                 single document converges extremely rapidly, typically
                 requiring update path lengths of under 15 nodes even
                 for large networks and very accurate solutions. This
                 implementation of PageRank provides a uniform ranking
                 scheme for documents in P2P systems, and its
                 integration with P2P keyword search provides one
                 solution to the network traffic problems engendered by
                 return of document hits. In basic P2P keyword search,
                 all the document hits must be returned to the querying
                 node causing large network traffic. An incremental
                 keyword search algorithm for P2P keyword search where
                 document hits are sorted by pagerank, and incrementally
                 returned to the querying node is proposed and
                 evaluated. Integration of this algorithm into P2P
                 keyword search can produce dramatic benefit both in
                 terms of effectiveness for users and decrease in
                 network traffic. The incremental search algorithm
                 provided approximately a ten-fold reduction in network
                 traffic for two-word and three-word queries.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8591",
}

@Article{Sankaralingam:2003:PCK,
  author =       "Karthikeyan Sankaralingam and Madhulika Yalamanchi and
                 Simha Sethumadhavan and James C. Browne",
  title =        "{Pagerank} Computation and Keyword Search on
                 Distributed Systems and {P2P} Networks",
  journal =      j-J-GRID-COMP,
  volume =       "1",
  number =       "3",
  pages =        "291--307",
  month =        "????",
  year =         "2003",
  CODEN =        "????",
  ISSN =         "1570-7873 (print), 1572-9184 (electronic)",
  ISSN-L =       "1570-7873",
  bibdate =      "Sat Dec 4 11:39:32 MST 2004",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.wkap.nl/jrnltoc.htm/1570-7873",
  URL =          "http://www.math.utah.edu/pub/tex/bib/jgridcomp.bib;
                 http://ipsapp008.kluweronline.com/IPS/content/ext/x/J/6160/I/9/A/6/abstract.htm",
  abstract =     "This paper presents a fully distributed computation
                 for Google's pagerank algorithm. The computation is
                 based on solution of the matrix equation defining
                 pageranks by a distributed implementation of
                 asynchronous iteration. Pageranks for the documents
                 stored on a web server or on a host in a peer-to-peer
                 network are computed in place and stored with the
                 documents. The matrix is never assembled and no crawls
                 of the web are required. Continuously accurate
                 pageranks are enabled by incremental computation of
                 pageranks for documents as they are inserted onto a
                 network storage host and incremental recomputation of
                 pageranks when documents are deleted. Intrahost and
                 intradomain dominance of document link structure is
                 naturally exploited by the distributed asynchronous
                 iteration algorithm.\par

                 Three implementations: (i) a simulation which was
                 previously reported, (ii) an implementation of the
                 algorithm in a peer-to-peer computational system and
                 (iii) an embedding of the computation in web servers,
                 are described. Application of the three implementations
                 to three different workloads, two constructed following
                 power law network models for link distributions and one
                 derived from the Government document database are
                 reported. Convergence for computation of a complete set
                 of pageranks is rapid: 1\% accuracy in 10 or fewer
                 messages per document. Incremental computation of
                 pageranks resulting from addition or deletion of
                 documents also converges rapidly, usually requiring 10
                 or fewer messages per document. Coupling locally stored
                 pageranks with the documents in a peer-to-peer network
                 dramatically diminishes the volume of data which must
                 be transmitted to satisfy keyword searches in
                 peer-to-peer networks.\par

                 The web server implementation shows that the
                 distributed algorithm can be used to enable web servers
                 to compute pageranks for the documents they store and
                 thus potentially enable effective keyword searches for
                 the documents stored on the web servers of intranets by
                 utilizing unused processing power of the web servers.",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Grid Computing",
  journal-URL =  "http://link.springer.com/journal/10723",
}

@InProceedings{Shi:2003:DPR,
  author =       "ShuMing Shi and Jin Yu and GuangWen Yang and DingXing
                 Wang",
  title =        "Distributed page ranking in structured {P2P}
                 networks",
  crossref =     "Yang:2003:ICP",
  pages =        "179--186",
  year =         "2003",
  DOI =          "http://dx.doi.org/10.1109/ICPP.2003.1240579",
  bibdate =      "Thu May 06 13:52:53 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We discuss the techniques of performing distributed
                 page ranking on top of structured peer-to-peer
                 networks. Distributed page ranking are needed because
                 the size of the Web grows at a remarkable speed and
                 centralized page ranking is not scalable. Open system
                 PageRank is presented based on the traditional PageRank
                 used by Google. We then propose some distributed page
                 ranking algorithms, partially prove their convergence,
                 and discuss some interesting properties of them.
                 Indirect transmission is introduced to reduce
                 communication overhead between page rankers and to
                 achieve scalable communication. The relationship
                 between convergence time and bandwidth consumed is also
                 discussed. Finally, we verify some of the discussions
                 by experiments based on real datasets.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Tao:2003:QSS,
  author =       "Wen-Xue Tao and Wan-Li Zuo",
  booktitle =    "{International Conference on Machine Learning and
                 Cybernetics, 2003}",
  title =        "Query-sensitive self-adaptable {Web} page ranking
                 algorithm",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "413--418",
  year =         "2003",
  ISBN =         "0-7803-8131-9",
  ISBN-13 =      "978-0-7803-8131-5",
  LCCN =         "????",
  bibdate =      "Thu May 06 13:40:45 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "This paper analyzes HITS and PageRank, two
                 representative examples of current Web page ranking
                 algorithms, and points out their limitations in
                 capturing both global and local importance scopes. A
                 detailed discussion is also conducted regarding the
                 reasons why manually setting topics adopted by
                 topic-sensitive PageRank algorithm cannot resolve the
                 same problem. Based on the above observation, a new
                 query-sensitive algorithm termed QS page-rank
                 satisfying both global and local authority is
                 introduced, and several strategies for combining our
                 algorithm with traditional PageRank are also proposed.
                 Experiment results show effectiveness of the new page
                 ranking algorithm.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Acharyya:2004:OEP,
  author =       "Sreangsu Acharyya and Joydeep Ghosh",
  editor =       "{ACM}",
  booktitle =    "{International World Wide Web Conference Proceedings
                 of the 13th international World Wide Web conference:
                 Alternate track papers \& posters}",
  title =        "Outlink estimation for {PageRank} computation under
                 missing data",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "486--487",
  year =         "2004",
  ISBN =         "1-58113-912-8",
  ISBN-13 =      "978-1-58113-912-9",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:05 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The enormity and rapid growth of the web-graph forces
                 quantities such as its pagerank to be computed under
                 missing information consisting of outlinks of pages
                 that have not yet been crawled. This paper examines the
                 role played by the size and distribution of this
                 missing data in determining the accuracy of the
                 computed pagerank, focusing on questions such as (i)
                 the accuracy of pageranks under missing information,
                 (ii) the size at which a crawl process may be aborted
                 while still ensuring reasonable accuracy of pageranks,
                 and (iii) algorithms to estimate pageranks under such
                 missing information. The first couple of questions are
                 addressed on the basis of certain simple bounds
                 relating the expected distance between the true and
                 computed pageranks and the size of the missing data.
                 The third question is explored by devising algorithms
                 to predict the pageranks when full information is not
                 available. A key feature of the 'dangling link
                 estimation' and 'clustered link estimation' algorithms
                 proposed is that, they do not need to run the pagerank
                 iteration afresh once the outlinks have been
                 estimated.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Altman:2004:RSP,
  author =       "Alon Altman",
  booktitle =    "????",
  title =        "Ranking systems: the {PageRank} axioms",
  volume =       "05011",
  publisher =    "Internat. Begegnungs- und Forschungszentrum f{\"u}r
                 Informatik",
  year =         "2004",
  bibdate =      "Fri Feb 19 15:35:56 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings",
  acknowledgement = ack-nhfb,
}

@Misc{Anonymous:2004:BGB,
  author =       "Anonymous",
  title =        "Biography: The {Google} boys",
  howpublished = "A\&E Television Networks",
  address =      "United States",
  day =          "18",
  month =        dec,
  year =         "2004",
  bibdate =      "Fri Jun 3 09:47:20 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  note =         "1 50-minute VHS videocassette.",
  abstract =     "Profiles Sergey Brin and Larry Page, two Stanford
                 University computer science Ph.D candidates who went on
                 to develop the world's most popular search engine.",
  acknowledgement = ack-nhfb,
  subject =      "Brin, Sergey; Page, Larry; Internet industry; United
                 States; History; Businesspeople; Biography",
  subject-dates = "1973--; 1973--",
}

@InProceedings{Boldi:2004:DYW,
  author =       "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
  title =        "Do your worst to make the best: Paradoxical effects in
                 {PageRank} incremental computations",
  crossref =     "Leonardi:2004:AMW",
  pages =        "168--180",
  year =         "2004",
  MRclass =      "68M10",
  bibdate =      "Thu May 06 12:24:30 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1109.68325",
  acknowledgement = ack-nhfb,
}

@InProceedings{Broder:2004:EPA,
  author =       "Andrei Z. Broder and Ronny Lempel and Farzin Maghoul
                 and Jan Pedersen",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceedings of
                 the 13th international World Wide Web conference:
                 Alternate track papers \& posters",
  title =        "Efficient {PageRank} approximation via graph
                 aggregation",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "484--485",
  year =         "2004",
  DOI =          "http://doi.acm.org/10.1145/1013367.1013537",
  ISBN =         "1-58113-912-8",
  ISBN-13 =      "978-1-58113-912-9",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We present a framework for approximating random-walk
                 based probability distributions over Web pages using
                 graph aggregation. We (1) partition the Web's graph
                 into classes of quasi-equivalent vertices, (2) project
                 the page-based random walk to be approximated onto
                 those classes, and (3) compute the stationary
                 probability distribution of the resulting class-based
                 random walk. From this distribution we can quickly
                 reconstruct a distribution on pages. In particular, our
                 framework can approximate the well-known PageRank
                 distribution by setting the classes according to the
                 set of pages on each Web host. We experimented on a
                 Web-graph containing over 1.4 billion pages, and were
                 able to produce a ranking that has Spearman rank-order
                 correlation of 0.95 with respect to PageRank. A
                 simplistic implementation of our method required less
                 than half the running time of a highly optimized
                 implementation of PageRank, implying that larger
                 speedup factors are probably possible.",
  acknowledgement = ack-nhfb,
  keywords =     "link analysis; search engines; web information
                 retrieval",
}

@InProceedings{Chen:2004:LME,
  author =       "Yen-Yu Chen and Qingqing Gan and Torsten Suel",
  editor =       "{ACM}",
  booktitle =    "Conference on Information and Knowledge Management
                 Proceedings of the thirteenth ACM international
                 conference on Information and knowledge management",
  title =        "Local methods for estimating {PageRank} values",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "381--389",
  year =         "2004",
  DOI =          "http://dx.doi.org/10.1145/383952.384003",
  ISBN =         "1-58113-874-1",
  ISBN-13 =      "978-1-58113-874-0",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The Google search engine uses a method called
                 PageRank, together with term-based and other ranking
                 techniques, to order search results returned to the
                 user. PageRank uses link analysis to assign a global
                 importance score to each web page. The PageRank scores
                 of all the pages are usually determined off-line in a
                 large-scale computation on the entire hyperlink graph
                 of the web, and several recent studies have focused on
                 improving the efficiency of this computation, which may
                 require multiple hours on a workstation. \par

                 However, in some scenarios, such as online analysis of
                 link evolution and mining of large web archives such as
                 the Internet Archive, it may be desirable to quickly
                 approximate or update the PageRanks of individual nodes
                 without performing a large-scale computation on the
                 entire graph. We address this problem by studying
                 several methods for efficiently estimating the PageRank
                 score of a particular web page using only a small
                 subgraph of the entire web. In our model, we assume
                 that the graph is accessible remotely via a link
                 database (such as the AltaVista Connectivity Server) or
                 is stored in a relational database that performs
                 lookups on disks to retrieve node and connectivity
                 information. We show that a reasonable estimate of the
                 PageRank value of a node is possible in most cases by
                 retrieving only a moderate number of nodes in the local
                 neighborhood of the node.",
  acknowledgement = ack-nhfb,
  keywords =     "external memory algorithms; link database; link-based
                 ranking; out-of-core; pagerank; search engines",
}

@InProceedings{Chirita:2004:FRP,
  author =       "P. Chirita and D. Olmedilla and W. Nejdl",
  title =        "Finding Related Pages Using the Link Structure of the
                 {WWW}",
  crossref =     "Zhong:2004:IWS",
  pages =        "632--635",
  year =         "2004",
  DOI =          "http://dx.doi.org/10.1109/WI.2004.10056",
  bibdate =      "Thu May 06 14:07:03 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@Article{Ding:2004:LAH,
  author =       "Chris H. Q. Ding and Hongyuan Zha and Xiaofeng He and
                 Parry Husbands and Horst D. Simon",
  title =        "Link Analysis: Hubs and Authorities on the {World Wide
                 Web}",
  journal =      j-SIAM-REVIEW,
  volume =       "46",
  number =       "2",
  pages =        "256--268",
  month =        jun,
  year =         "2004",
  CODEN =        "SIREAD",
  DOI =          "http://dx.doi.org/10.1137/S0036144501389218",
  ISSN =         "0036-1445 (print), 1095-7200 (electronic)",
  ISSN-L =       "0036-1445",
  bibdate =      "Sat Apr 16 12:47:29 MDT 2005",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SIREV/46/2;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://epubs.siam.org/sam-bin/dbq/article/38921",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Review",
  journal-URL =  "http://epubs.siam.org/sirev",
}

@TechReport{Gleich:2004:FPP,
  author =       "D. Gleich and L. Zhukov and P. Berkhin",
  title =        "Fast Parallel {PageRank}: {A} Linear System Approach",
  type =         "Technical Report",
  number =       "YRL-2004-038",
  institution =  "Yahoo! Research",
  address =      "????",
  year =         "2004",
  bibdate =      "Wed Nov 30 08:08:31 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Ingongngam:2004:TCA,
  author =       "P. Ingongngam and A. Rungsawang",
  title =        "Topic-centric algorithm: a novel approach to {Web}
                 link analysis",
  crossref =     "Barolli:2004:ICA",
  pages =        "299--301",
  year =         "2004",
  DOI =          "http://dx.doi.org/10.1109/AINA.2004.1283807",
  bibdate =      "Thu May 06 14:11:27 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@Misc{Kamvar:2004:ACR,
  author =       "Sepandar D. Kamvar and Taher H. Haveliwala and Gene H.
                 Golub",
  title =        "Adaptive computation of ranking",
  howpublished = "US Patent 7,028,029.",
  day =          "23",
  month =        aug,
  year =         "2004",
  bibdate =      "Wed Jun 01 18:43:31 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://patft.uspto.gov/netahtml/PTO/srchnum.htm",
  abstract =     "A system and method is disclosed in which a ranking
                 function for a set of document rank values is
                 iteratively solved with respect to a set of linked
                 documents until a first stability condition is
                 satisfied. After such condition is satisfied, some of
                 the ranks will have converged. The ranking function is
                 modified to take into account these converged ranks so
                 as to reduce the ranking function's computation cost.
                 The modified ranking function is then solved until a
                 second stability condition is satisfied. After such
                 condition is satisfied more of the ranks will have
                 converged. The ranking function is again modified and
                 process continues until complete.",
  acknowledgement = ack-nhfb,
}

@Article{Kamvar:2004:AMC,
  author =       "Sepandar Kamvar and Taher Haveliwala and Gene Golub",
  title =        "Adaptive methods for the computation of {PageRank}",
  journal =      j-LINEAR-ALGEBRA-APPL,
  volume =       "386",
  number =       "1",
  pages =        "51--65",
  day =          "15",
  month =        jul,
  year =         "2004",
  CODEN =        "LAAPAW",
  DOI =          "http://dx.doi.org/10.1016/j.laa.2003.12.008",
  ISSN =         "0024-3795 (print), 1873-1856 (electronic)",
  ISSN-L =       "0024-3795",
  MRclass =      "60-04 (60G50 60J10)",
  MRnumber =     "MR2066607",
  bibdate =      "Tue Nov 9 07:02:36 MST 2004",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.sciencedirect.com/science/journal/00243795",
  URL =          "http://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib;
                 http://www.math.utah.edu/pub/tex/bib/linala2000.bib",
  ZMnumber =     "1091.68044",
  acknowledgement = ack-nhfb,
  fjournal =     "Linear Algebra and its Applications",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00243795",
  keywords =     "Google search engine; PageRank algorithm",
}

@Article{Langville:2004:DIP,
  author =       "Amy N. Langville and Carl D. Meyer",
  title =        "Deeper inside {PageRank}",
  journal =      j-INTERNET-MATH,
  volume =       "1",
  number =       "3",
  pages =        "335--380",
  year =         "2004",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35",
  MRnumber =     "MR2111012",
  bibdate =      "Wed May 5 19:27:49 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1109190965",
  ZMnumber =     "1098.68010",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@InProceedings{Langville:2004:UPI,
  author =       "Amy Nicole Langville and Carl Dean Meyer",
  editor =       "{ACM}",
  booktitle =    "{Proceedings of the 13th international World Wide Web
                 conference: Alternate track papers \& posters}",
  title =        "Updating {PageRank} with iterative aggregation",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "392--393",
  year =         "2004",
  DOI =          "http://dx.doi.org/10.1137/1031050",
  ISBN =         "1-58113-912-8",
  ISBN-13 =      "978-1-58113-912-9",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We present an algorithm for updating the PageRank
                 vector [1]. Due to the scale of the web, Google only
                 updates its famous PageRank vector on a monthly basis.
                 However, the Web changes much more frequently.
                 Drastically speeding the PageRank computation can lead
                 to fresher, more accurate rankings of the webpages
                 retrieved by search engines. It can also make the goal
                 of real-time personalized rankings within reach. On two
                 small subsets of the web, our algorithm updates
                 PageRank using just 25\% and 14\%, respectively, of the
                 time required by the original PageRank algorithm. Our
                 algorithm uses iterative aggregation techniques [7, 8]
                 to focus on the slow-converging states of the Markov
                 chain. The most exciting feature of this algorithm is
                 that it can be joined with other PageRank acceleration
                 methods, such as the dangling node lumpability
                 algorithm [6], quadratic extrapolation [4], and
                 adaptive PageRank [3], to realize even greater speedups
                 (potentially a factor of 60 or more speedup when all
                 algorithms are combined). every few weeks. Our solution
                 harnesses the power of iterative aggregation principles
                 for Markov chains to allow for much more frequent
                 updates to the valuable ranking vectors.",
  acknowledgement = ack-nhfb,
  keywords =     "aggregation; disaggregation; link analysis; Markov
                 chains; pagerank; power method; stationary vector;
                 updating",
}

@InProceedings{Manaskasemsak:2004:PPC,
  author =       "Bundit Manaskasemsak and Arnon Rungsawang",
  title =        "Parallel {PageRank} computation on a gigabit {PC}
                 cluster",
  crossref =     "Barolli:2004:ICA",
  volume =       "1",
  pages =        "273--277",
  year =         "2004",
  DOI =          "http://dx.doi.org/10.1109/AINA.2004.1283923",
  bibdate =      "Fri Feb 19 18:16:05 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1283923",
  abstract =     "Efficient computing the PageRank scores for a large
                 web graph is actually one of the hot issues in Web-IR
                 community. Recent researches propose to accelerate the
                 computation, both in algorithmic and architectural
                 ways. We here focus on a parallel PageRank
                 computational architecture on a cluster of Opteron PCs
                 networked via a Gigabit Ethernet. We propose both an
                 efficient parallel algorithm of the standard PageRank
                 computation, and a simple pairwise communication model
                 needed to synchronize local PageRank scores between
                 processors. Our experimental results conducted on a
                 large web graph, over 1.5 billion links, synthesized
                 from the real set of crawled web pages in the TH
                 domain, are quite promising. The current implementation
                 takes less than15 seconds for an iteration run.",
  acknowledgement = ack-nhfb,
}

@Article{Markarian:2004:IEN,
  author =       "Roberto Markarian and Nelson M{\"o}ller",
  title =        "The importance of each node in a structure of links:
                 {Google PageRank}",
  journal =      "Bol. Asoc. Mat. Venez.",
  volume =       "11",
  number =       "2",
  pages =        "233--252",
  year =         "2004",
  CODEN =        "????",
  ISSN =         "1315-4125",
  MRclass =      "68U35 (15A18)",
  MRnumber =     "MR2139430",
  bibdate =      "Wed May 5 19:27:59 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1086.68658",
  acknowledgement = ack-nhfb,
  fjournal =     "Bolet\'\i n de la Asociaci\'on Matem\'atica
                 Venezolana",
}

@InProceedings{Meng:2004:ELA,
  author =       "Tao Meng and Hongfei Yan and Jimin Wang and Xiaoming
                 Li",
  title =        "The Evolution of Link-Attributes for Pages and Its
                 Implications on Web Crawling",
  crossref =     "Zhong:2004:IWS",
  pages =        "578--581",
  year =         "2004",
  bibdate =      "Thu May 06 14:14:46 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Mihalcea:2004:PSN,
  author =       "Rada Mihalcea and Paul Tarau and Elizabeth Figa",
  editor =       "Margaret King and others",
  booktitle =    "{Coling Geneva 2004: 20th International Conference on
                 Computational Linguistics, August 23rd to 27th, 2004:
                 proceedings}",
  title =        "{PageRank} on semantic networks, with application to
                 word sense disambiguation",
  publisher =    "Association for Computational Linguistics",
  address =      "Morristown, NJ, USA",
  pages =        "??--??",
  year =         "2004",
  DOI =          "http://dx.doi.org/10.3115/1220355.1220517",
  ISBN =         "1-932432-48-5",
  ISBN-13 =      "978-1-932432-48-0",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.cs.unt.edu/~rada/papers/mihalcea.coling04.pdf",
  abstract =     "This paper presents a new open text word sense
                 disambiguation method that combines the use of logical
                 inferences with PageRank-style algorithms applied on
                 graphs extracted from natural language documents. We
                 evaluate the accuracy of the proposed algorithm on
                 several sense-annotated texts, and show that it
                 consistently outperforms the accuracy of other
                 previously proposed knowledge-based word sense
                 disambiguation methods. We also explore and evaluate
                 methods that combine several open-text word sense
                 disambiguation algorithms.",
  acknowledgement = ack-nhfb,
  bookpages =    "xvi + 763",
  pagecount =    "7",
}

@InProceedings{Suzuki:2004:HDP,
  author =       "K. Suzuki",
  title =        "How does propagational investment currency system
                 change the world?",
  crossref =     "IEEE:2004:SWI",
  pages =        "9--15",
  year =         "2004",
  DOI =          "http://dx.doi.org/10.1109/SAINTW.2004.1268559",
  bibdate =      "Thu May 06 14:02:50 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Wang:2004:CPD,
  author =       "Yuan Wang and David J. DeWitt",
  editor =       "Mario A. Nascimento",
  booktitle =    "{Proceedings of the thirtieth International Conference
                 on Very Large Data Bases: Toronto, Canada, August
                 31--September 3, 2004}",
  title =        "Computing {PageRank} in a distributed {Internet}
                 search system",
  volume =       "30",
  publisher =    pub-MORGAN-KAUFMANN,
  address =      pub-MORGAN-KAUFMANN:adr,
  pages =        "420--431",
  year =         "2004",
  DOI =          "http://dx.doi.org/10.1145/383059.383071",
  ISBN =         "0-12-088469-0",
  ISBN-13 =      "978-0-12-088469-8",
  LCCN =         "QA76.9.D3 I559 2004",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Existing Internet search engines use web crawlers to
                 download data from the Web. Page quality is measured on
                 central servers, where user queries are also processed.
                 This paper argues that using crawlers has a list of
                 disadvantages. Most importantly, crawlers do not scale.
                 Even Google, the leading search engine, indexes less
                 than 1\% of the entire Web. This paper proposes a
                 distributed search engine framework, in which every web
                 server answers queries over its own data. Results from
                 multiple web servers will be merged to generate a
                 ranked hyperlink list on the submitting server. This
                 paper presents a series of algorithms that compute
                 PageRank in such framework. The preliminary experiments
                 on a real data set demonstrate that the system achieves
                 comparable accuracy on PageRank vectors to Google's
                 well-known PageRank algorithm and, therefore, high
                 quality of query results.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Xing:2004:WPA,
  author =       "W. Xing and A. Ghorbani",
  booktitle =    "{Proceedings of the Second Annual Conference on
                 Communication Networks and Services Research (2004)}",
  title =        "Weighted {PageRank} Algorithm",
  crossref =     "Ghorbani:2004:PAC",
  pages =        "305--314",
  year =         "2004",
  DOI =          "http://dx.doi.org/10.1109/DNSR.2004.1344743",
  ISBN =         "0-7695-2096-0",
  ISBN-13 =      "978-0-7695-2096-4",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1344743",
  abstract =     "With the rapid growth of the Web, users get easily
                 lost in the rich hyper structure. Providing relevant
                 information to the users to cater to their needs is the
                 primary goal of website owners. Therefore, finding the
                 content of the Web and retrieving the users' interests
                 and needs from their behavior have become increasingly
                 important. Web mining is used to categorize users and
                 pages by analyzing the users' behavior,the content of
                 the pages, and the order of the URLs that tend to be
                 accessed in order. Web structure mining plays an
                 important role in this approach. Two page ranking
                 algorithms, HITS and PageRank, are commonly used in web
                 structure mining. Both algorithms treat all links
                 equally when distributing rank scores. Several
                 algorithms have been developed to improve the
                 performance of these methods. The Weighted PageRank
                 algorithm (WPR), an extension to the standard PageRank
                 algorithm, is introduced in this paper. WPR takes into
                 account the importance of both the inlinks and the
                 outlinks of the pages and distributes rank scores based
                 on the popularity of the pages. The results of our
                 simulation studies show that WPR performs better than
                 the conventional PageRank algorithm in terms of
                 returning larger number of relevant pages to a given
                 query.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9316",
  keywords =     "HITS; PageRank; Web Mining; Web Structure Mining;
                 Weighted PageRank",
}

@InProceedings{Yamamoto:2004:DPD,
  author =       "A. Yamamoto and D. Asahara and T. Itao and S. Tanaka
                 and T. Suda",
  title =        "Distributed {PageRank}: a distributed reputation model
                 for open peer-to-peer network",
  crossref =     "IEEE:2004:SWI",
  pages =        "389--394",
  year =         "2004",
  DOI =          "http://dx.doi.org/10.1109/SAINTW.2004.1268664",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1268664",
  abstract =     "This paper proposes a distributed reputation model for
                 open peer-to-peer networks called distributed pagerank.
                 This model is motivated by the observation that
                 although pagerank has already satisfied the
                 requirements of reputation models, the centralized
                 calculation of pagerank is incompatible with
                 peer-to-peer networks. Distributed pagerank is a
                 decentralized approach for calculating the pagerank of
                 each peer by its reputation, in which the relationship
                 between peers is introduced as the equivalent to the
                 link between web pages. The distributed calculation of
                 pagerank is performed asynchronously by each peer as it
                 communicates with the other peers. The asynchronous
                 calculation accomplishes both demanding no extra
                 messages for the calculation of pagerank and steadily
                 calculating an accurate pagerank of each peer even
                 under the dynamic topology of relationships. The result
                 of the simulation has indicated that the calculated
                 pagerank value of each peer converges at the original
                 pagerank value under the static topology of
                 relationships, which is presumable under a dynamic
                 topology. A fully implemented application of
                 distributed pagerank has also been presented, which
                 supports dynamic formation of communities with
                 reputation ranking.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8957",
}

@InBook{Altman:2005:RSPa,
  editor =       "Alon Altman",
  title =        "Ranking systems: the {PageRank} axioms",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "??--??",
  year =         "2005",
  ISBN =         "????",
  ISBN-13 =      "????",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 10:03:23 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings 05011",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2005/197/pdf/05011.AltmanAlon.Paper",
  acknowledgement = ack-nhfb,
}

@InProceedings{Altman:2005:RSPb,
  author =       "A. Altman and M. Tennenholtz",
  booktitle =    "{EC '05: proceedings of the 6th ACM Conference on
                 Electronic Commerce, Vancouver, Canada, June 5--8,
                 2005}",
  title =        "Ranking systems: the {PageRank} axioms",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "1--8",
  year =         "2005",
  ISBN =         "1-59593-049-3",
  ISBN-13 =      "978-1-59593-049-1",
  LCCN =         "HF5548.32 .A26 2005",
  bibdate =      "Tue Jul 20 16:00:08 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  bookpages =    "viii + 294",
  keywords =     "PageRank",
}

@InProceedings{Benczur:2005:FLR,
  author =       "Andr{\'a}s A. Bencz{\'u}r and K{\'a}roly Csalog{\'a}ny
                 and Tam{\'a}s Sarl{\'o}s",
  editor =       "{ACM}",
  booktitle =    "{Special interest tracks and posters of the 14th
                 international conference on World Wide Web}",
  title =        "On the feasibility of low-rank approximation for
                 personalized {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "972--973",
  year =         "2005",
  DOI =          "http://doi.acm.org/10.1145/1062745.1062824",
  ISBN =         "1-59593-051-5",
  ISBN-13 =      "978-1-59593-051-4",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Personalized PageRank expresses backlink-based page
                 quality around user-selected pages in a similar way to
                 PageRank over the entire Web. Algorithms for computing
                 personalized PageRank on the fly are either limited to
                 a restricted choice of page selection or believed to
                 behave well only on sparser regions of the Web. In this
                 paper we show the feasibility of computing personalized
                 PageRank by a $k < 1000$ low-rank approximation of the
                 Page-Rank transition matrix; by our algorithm we may
                 compute an approximate personalized Page-Rank by
                 multiplying an $n \times k$, a $k \times n$ matrix and
                 the $n$-dimensional personalization vector. Since
                 low-rank approximations are accurate on dense regions,
                 we hope that our technique will combine well with known
                 algorithms.",
  acknowledgement = ack-nhfb,
  keywords =     "link analysis; low-rank approximation; personalized
                 PageRank; singular value decomposition; web information
                 retrieval",
}

@Article{Berkhin:2005:SPC,
  author =       "Pavel Berkhin",
  title =        "A survey on {PageRank} computing",
  journal =      j-INTERNET-MATH,
  volume =       "2",
  number =       "1",
  pages =        "73--120",
  year =         "2005",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35",
  MRnumber =     "MR2166277 (2006c:68180)",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1128530802",
  ZMnumber =     "1100.68504",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@Book{Berry:2005:USE,
  author =       "Michael W. Berry and Murray Browne",
  title =        "Understanding search engines: mathematical modeling
                 and text retrieval",
  publisher =    pub-SIAM,
  address =      pub-SIAM:adr,
  edition =      "Second",
  pages =        "xvii + 117",
  year =         "2005",
  ISBN =         "0-89871-581-4",
  ISBN-13 =      "978-0-89871-581-1",
  LCCN =         "TK5105.884 .B47 2005",
  bibdate =      "Fri Jun 3 10:03:23 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  subject =      "Web search engines; Vector spaces; Text processing
                 (Computer science)",
  tableofcontents = "Preface to the Second Edition \\
                 1. Introduction \\
                 1.1. Document file preparation \\
                 1.1.1. Manual indexing \\
                 1.1.2. File cleanup \\
                 1.2. Information extraction \\
                 1.3. Vector space modeling \\
                 1.4. Matrix decompositions \\
                 1.5. Query representations \\
                 1.6. Ranking and relevance feedback \\
                 1.7. Searching by link structure \\
                 1.8. User interface \\
                 1.9. Book format \\
                 2. Document file preparation \\
                 2.1. Document purification and analysis \\
                 2.1.1. Text formatting \\
                 2.1.2. Validation \\
                 2.2. Manual indexing \\
                 2.3. Automatic indexing \\
                 2.4. Item normalization \\
                 2.5. Inverted file structures \\
                 2.5.1. Document file \\
                 2.5.2. Dictionary list \\
                 2.5.3. Inversion list \\
                 2.5.4. Other file structures \\
                 3. Vector space models \\
                 3.1. Construction \\
                 3.1.1. Term-by-document matrices \\
                 3.1.2. Simple Query matching \\
                 3.2. Design issues \\
                 3.2.1. Term weighting \\
                 3.2.2. Sparse matrix storage \\
                 3.2.3. Low-rank approximations \\
                 4. Matrix decompositions \\
                 4.1. QR factorization \\
                 4.2. Singular value decomposition \\
                 4.2.1. Low-rank approximations \\
                 4.2.2. Query matching \\
                 4.2.3. Software \\
                 4.3. Semidiscrete decomposition \\
                 4.4. Updating techniques \\
                 5. Query management \\
                 5.1. Query binding \\
                 5.2. Types of queries \\
                 5.2.1. Boolean queries \\
                 5.2.2. Natural language queries \\
                 5.2.3. Thesaurus queries \\
                 5.2.4. Fuzzy queries \\
                 5.2.5. Term searches \\
                 5.2.6. Probabilistic queries \\
                 6. Ranking and relevance feedback \\
                 6.1. Performance evaluation \\
                 6.1.1. Precision \\
                 6.1.2. Recall \\
                 6.1.3. Average precision \\
                 6.1.4. Genetic algorithms \\
                 6.2. Relevance feedback \\
                 7. Searching by link structure \\
                 7.1. HITS method \\
                 7.1.1. HITS implementation \\
                 7.1.2. HITS summary \\
                 7.2. PageRank method \\
                 7.2.1. PageRank adjustments \\
                 7.2.2. PageRank implementation \\
                 7.2.3. PageRank summary \\
                 8. User interface considerations \\
                 8.1. General guidelines \\
                 8.2. Search engine interfaces \\
                 8.2.1. Form fill-in \\
                 8.2.2. Display considerations \\
                 8.2.3. Progress indication \\
                 8.2.4. No penalties for error \\
                 8.2.5. Results \\
                 8.2.6. Test and retest \\
                 8.2.7. Final considerations \\
                 9. Further reading \\
                 9.1. General textbooks on IR \\
                 9.2. Computational methods and software \\
                 9.3. Search engines \\
                 9.4. User interfaces \\
                 Bibliography \\
                 Index",
}

@Article{Bianchini:2005:IP,
  author =       "Monica Bianchini and Marco Gori and Franco
                 Scarselli",
  title =        "Inside {PageRank}",
  journal =      j-TOIT,
  volume =       "5",
  number =       "1",
  pages =        "92--128",
  month =        feb,
  year =         "2005",
  CODEN =        "????",
  DOI =          "http://dx.doi.org/10.1016/S0169-7552(98)00061-0",
  ISSN =         "1533-5399 (print), 1557-6051 (electronic)",
  ISSN-L =       "1533-5399",
  bibdate =      "Thu Apr 14 10:31:40 MDT 2005",
  bibsource =    "http://www.acm.org/pubs/contents/journals/toit/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/toit.bib",
  abstract =     "Although the interest of a Web page is strictly
                 related to its content and to the subjective readers'
                 cultural background, a measure of the page authority
                 can be provided that only depends on the topological
                 structure of the Web. PageRank is a noticeable way to
                 attach a score to Web pages on the basis of the Web
                 connectivity. In this article, we look inside PageRank
                 to disclose its fundamental properties concerning
                 stability, complexity of computational scheme, and
                 critical role of parameters involved in the
                 computation. Moreover, we introduce a circuit analysis
                 that allows us to understand the distribution of the
                 page score, the way different Web communities interact
                 each other, the role of dangling pages (pages with no
                 outlinks), and the secrets for promotion of Web
                 pages.",
  acknowledgement = ack-nhfb,
  fjournal =     "ACM Transactions on Internet Technology (TOIT)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J780",
  keywords =     "Information retrieval; Markov chains; PageRank; search
                 engines; searching the Web; Web page scoring",
}

@Article{Boldi:2005:PEP,
  author =       "Paolo Boldi and Massimo Santini and Sebastiano
                 Vigna",
  title =        "Paradoxical effects in {PageRank} incremental
                 computations",
  journal =      j-INTERNET-MATH,
  volume =       "2",
  number =       "3",
  pages =        "387--404",
  year =         "2005",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (05C80 68R10)",
  MRnumber =     "MR2212371 (2006j:68129)",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1150474888",
  ZMnumber =     "1095.68503",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@InProceedings{Boldi:2005:PFD,
  author =       "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceedings of
                 the 14th international conference on World Wide Web",
  title =        "{PageRank} as a function of the damping factor",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "557--566",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1145/382979.383041",
  ISBN =         "1-59593-046-9",
  ISBN-13 =      "978-1-59593-046-0",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "PageRank is defined as the stationary state of a
                 Markov chain. The chain is obtained by perturbing the
                 transition matrix induced by a web graph with a damping
                 factor $\alpha$ that spreads uniformly part of the
                 rank. The choice of $\alpha$ is eminently empirical,
                 and in most cases the original suggestion $\alpha$ =
                 0.85 by Brin and Page is still used. Recently, however,
                 the behaviour of PageRank with respect to changes in
                 $\alpha$ was discovered to be useful in link-spam
                 detection[21]. Moreover, an analytical justification of
                 the value chosen for $\alpha$ is still missing. In this
                 paper, we give the first mathematical analysis of
                 PageRank when $\alpha$ changes. In particular, we show
                 that, contrarily to popular belief, for real-world
                 graphs values of $\alpha$ close to 1 do not give a more
                 meaningful ranking. Then, we give closed-form formulae
                 for PageRank derivatives of any order, and an extension
                 of the Power Method that approximates them with
                 convergence O (t k $\alpha$ t ) for the k-th
                 derivative. Finally, we show a tight connection between
                 iterated computation and analytical behaviour by
                 proving that the k-th iteration of the Power Method
                 gives exactly the PageRank value obtained using a
                 Maclaurin polynomial of degree k. The latter result
                 paves the way towards the application of analytical
                 methods to the study of PageRank.",
  acknowledgement = ack-nhfb,
  keywords =     "approximation; PageRank; Web graph",
}

@Article{Brezinski:2005:EMP,
  author =       "Claude Brezinski and Michela Redivo-Zaglia and Stefano
                 Serra-Capizzano",
  title =        "Extrapolation methods for {PageRank} computations",
  journal =      j-C-R-MATH-ACAD-SCI-PARIS,
  volume =       "340",
  number =       "5",
  pages =        "393--397",
  year =         "2005",
  CODEN =        "????",
  DOI =          "http://dx.doi.org/10.1016/j.crma.2005.01.015",
  ISSN =         "1631-073X",
  ISSN-L =       "1631-073X",
  MRclass =      "65F15",
  MRnumber =     "MR2127117",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1066.65040",
  acknowledgement = ack-nhfb,
  fjournal =     "Comptes Rendus Math\'ematique. Acad\'emie des
                 Sciences. Paris",
}

@InProceedings{daCosta:2005:WSM,
  author =       "M. G. {da Costa, Jr.} and Zhiguo Gong",
  title =        "{Web} structure mining: an introduction",
  crossref =     "Meng:2005:IIC",
  pages =        "??--??",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/ICIA.2005.1635156",
  bibdate =      "Thu May 06 15:33:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@TechReport{DelCorso:2005:CKS,
  author =       "Gianna M. {Del Corso} and Antonio Gull{\'\i} and
                 Francesco Romani",
  title =        "Comparison of {Krylov} Subspace Methods on the
                 {PageRank} Problem",
  type =         "Technical Report",
  number =       "TR-05-20",
  institution =  "University of Pisa",
  address =      "Pisa, Italy",
  pages =        "????",
  year =         "2005",
  bibdate =      "Wed Nov 30 08:06:39 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@Article{DelCorso:2005:FPC,
  author =       "Gianna M. {Del Corso} and Antonio Gull{\'\i} and
                 Francesco Romani",
  title =        "Fast {PageRank} computation via a sparse linear
                 system",
  journal =      j-INTERNET-MATH,
  volume =       "2",
  number =       "3",
  pages =        "251--273",
  year =         "2005",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (05C80 65F50)",
  MRnumber =     "MR2212366 (2006j:68131)",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1150474883",
  ZMnumber =     "1095.68578",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@Article{Dominich:2005:PII,
  author =       "S{\'a}ndor Dominich and Adrienn Skrop",
  title =        "{PageRank} and Interaction Information Retrieval:
                 Research Articles",
  journal =      "Journal of the American Society for Information
                 Science and Technology",
  volume =       "56",
  number =       "1",
  pages =        "63--69",
  month =        jan,
  year =         "2005",
  CODEN =        "JASIEF",
  DOI =          "http://dx.doi.org/10.1002/asi.v56:1",
  ISSN =         "1532-2882 (print), 1532-2890 (electronic)",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The PageRank method is used by the Google Web search
                 engine to compute the importance of Web pages. Two
                 different views have been developed for the
                 interpretation of the PageRank method and values: (a)
                 stochastic (random surfer): the PageRank values can be
                 conceived as the steady-state distribution of a Markov
                 chain, and (b) algebraic: the PageRank values form the
                 eigenvector corresponding to eigenvalue 1 of the Web
                 link matrix. The Interaction Information Retrieval (I 2
                 R) method is a nonclassical information retrieval
                 paradigm, which represents a connectionist approach
                 based on dynamic systems. In the present paper, a
                 different interpretation of PageRank is proposed,
                 namely, a dynamic systems viewpoint, by showing that
                 the PageRank method can be formally interpreted as a
                 particular case of the Interaction Information
                 Retrieval method; and thus, the PageRank values may be
                 interpreted as neutral equilibrium points of the Web.",
  acknowledgement = ack-nhfb,
  ajournal =     "J. Am. Soc. Inf. Sci. Technol.",
  fjournal =     "Journal of the American Society for Information
                 Science and Technology",
}

@InProceedings{Eirinaki:2005:UBP,
  author =       "Magdalini Eirinaki and Michalis Vazirgiannis",
  title =        "Usage-based {PageRank} for {Web} personalization",
  crossref =     "Han:2005:FII",
  pages =        "130--137",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/ICDM.2005.148",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1565671",
  abstract =     "Recommendation algorithms aim at proposing 'next'
                 pages to a user based on her current visit and the past
                 users' navigational patterns. In the vast majority of
                 related algorithms, only the usage data are used to
                 produce recommendations, whereas the structural
                 properties of the Web graph are ignored. We claim that
                 taking also into account the web structure and using
                 link analysis algorithms ameliorates the quality of
                 recommendations. In this paper we present UPR, a novel
                 personalization algorithm which combines usage data and
                 link analysis techniques for ranking and recommending
                 web pages to the end user. Using the web site's
                 structure and its usage data we produce personalized
                 navigational graph synopses (prNG) to be used for
                 applying UPR and produce personalized recommendations.
                 Experimental results show that the accuracy of the
                 recommendations is superior to pure usage-based
                 approaches.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10470",
}

@Article{Farahat:2005:ARH,
  author =       "Ayman Farahat and Thomas LoFaro and Joel C. Miller and
                 Gregory Rae and Lesley A. Ward",
  title =        "Authority Rankings from {HITS}, {PageRank}, and
                 {SALSA}: Existence, Uniqueness, and Effect of
                 Initialization",
  journal =      j-SIAM-J-SCI-COMP,
  volume =       "27",
  number =       "4",
  pages =        "1181--1201",
  month =        jul,
  year =         "2005",
  CODEN =        "SJOCE3",
  DOI =          "http://dx.doi.org/10.1137/S1064827502412875",
  ISSN =         "1064-8275 (print), 1095-7197 (electronic)",
  ISSN-L =       "1064-8275",
  MRclass =      "68U35 (15A18 15A48 68R10 68W40)",
  MRnumber =     "MR2199745 (2006m:68169)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Tue Jun 27 09:24:24 MDT 2006",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SISC/27/4;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://epubs.siam.org/volume-27/art_41287.html",
  ZMnumber =     "1094.68111",
  abstract =     "Algorithms such as Kleinberg's HITS algorithm, the
                 PageRank algorithm of Brin and Page, and the SALSA
                 algorithm of Lempel and Moran use the link structure of
                 a network of web pages to assign weights to each page
                 in the network. The weights can then be used to rank
                 the pages as authoritative sources. These algorithms
                 share a common underpinning; they find a dominant
                 eigenvector of a nonnegative matrix that describes the
                 link structure of the given network and use the entries
                 of this eigenvector as the page weights. We use this
                 commonality to give a unified treatment, proving the
                 existence of the required eigenvector for the PageRank,
                 HITS, and SALSA algorithms, the uniqueness of the
                 PageRank eigenvector, and the convergence of the
                 algorithms to these eigenvectors. However, we show that
                 the HITS and SALSA eigenvectors need not be unique. We
                 examine how the initialization of the algorithms
                 affects the final weightings produced. We give examples
                 of networks that lead the HITS and SALSA algorithms to
                 return nonunique or nonintuitive rankings. We
                 characterize all such networks in terms of the
                 connectivity of the related HITS authority graph. We
                 propose a modification, Exponentiated Input to HITS, to
                 the adjacency matrix input to the HITS algorithm. We
                 prove that Exponentiated Input to HITS returns a unique
                 ranking, provided that the network is weakly connected.
                 Our examples also show that SALSA can give inconsistent
                 hub and authority weights, due to nonuniqueness. We
                 also mention a small modification to the SALSA
                 initialization which makes the hub and authority
                 weights consistent.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Scientific Computing",
  journal-URL =  "http://epubs.siam.org/sisc",
}

@Article{Fogaras:2005:TSF,
  author =       "D{\'a}niel Fogaras and Bal{\'a}zs R{\'a}cz and
                 K{\'a}roly Csalog{\'a}ny and Tam{\'a}s Sarl{\'o}s",
  title =        "Towards scaling fully personalized {PageRank}:
                 algorithms, lower bounds, and experiments",
  journal =      j-INTERNET-MATH,
  volume =       "2",
  number =       "3",
  pages =        "333--358",
  year =         "2005",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (05C80 68R10)",
  MRnumber =     "MR2212369 (2006j:68132)",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1150474886",
  ZMnumber =     "1095.68579",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@Article{Gori:2005:EAG,
  author =       "M. Gori and M. Maggini and L. Sarti",
  title =        "Exact and approximate graph matching using random
                 walks",
  journal =      j-IEEE-TRANS-PATT-ANAL-MACH-INTEL,
  volume =       "27",
  number =       "7",
  pages =        "1100--1111",
  month =        jul,
  year =         "2005",
  CODEN =        "ITPIDJ",
  DOI =          "http://dx.doi.org/10.1109/TPAMI.2005.138",
  ISSN =         "0162-8828",
  ISSN-L =       "0162-8828",
  bibdate =      "Thu May 06 14:59:25 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE Transactions on Pattern Analysis and Machine Intelligence",
  journal-URL =  "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34",
}

@Article{Higham:2005:GPM,
  author =       "Desmond J. Higham",
  title =        "{Google PageRank} as mean playing time for pinball on
                 the reverse web",
  journal =      j-APPL-MATH-LETT,
  volume =       "18",
  number =       "12",
  pages =        "1359--1362",
  year =         "2005",
  CODEN =        "AMLEEL",
  DOI =          "http://dx.doi.org/10.1016/j.aml.2005.02.020",
  ISSN =         "0893-9659 (print), 1873-5452 (electronic)",
  ISSN-L =       "0893-9659",
  MRclass =      "68U35 (60J10 60J20)",
  MRnumber =     "MR2189889",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1083.68509",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Mathematics Letters. An International Journal
                 of Rapid Publication",
  journal-URL =  "http://www.sciencedirect.com/science/journal/08939659",
}

@Article{Ipsen:2005:CAP,
  author =       "Ilse C. F. Ipsen and Steve Kirkland",
  title =        "Convergence Analysis of a {PageRank} Updating
                 Algorithm by {Langville} and {Meyer}",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "27",
  number =       "4",
  pages =        "952--967",
  year =         "2005",
  CODEN =        "SJMAEL",
  DOI =          "http://dx.doi.org/10.1137/S0895479804439808",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The PageRank updating algorithm proposed by Langville
                 and Meyer is a special case of an iterative
                 aggregation/disaggregation (SIAD) method for computing
                 stationary distributions of very large Markov chains.
                 It is designed, in particular, to speed up the
                 determination of PageRank, which is used by the search
                 engine Google in the ranking of web pages. In this
                 paper the convergence, in exact arithmetic, of the SIAD
                 method is analyzed. The SIAD method is expressed as the
                 power method preconditioned by a partial LU
                 factorization. This leads to a simple derivation of the
                 asymptotic convergence rate of the SIAD method. It is
                 known that the power method applied to the Google
                 matrix always converges, and we show that the
                 asymptotic convergence rate of the SIAD method is at
                 least as good as that of the power method. Furthermore,
                 by exploiting the hyperlink structure of the web it can
                 be shown that the asymptotic convergence rate of the
                 SIAD method applied to the Google matrix can be made
                 strictly faster than that of the power method.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
  keywords =     "aggregation/disaggregation; Google; Markov chain;
                 PageRank; power method; stochastic complement",
}

@InProceedings{Kolda:2005:HOW,
  author =       "T. G. Kolda and B. W. Bader and J. P. Kenny",
  title =        "Higher-order {Web} link analysis using multilinear
                 algebra",
  crossref =     "Han:2005:FII",
  pages =        "??--??",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/ICDM.2005.77",
  bibdate =      "Thu May 06 15:45:35 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Linear algebra is a powerful and proven tool in Web
                 search. Techniques, such as the PageRank algorithm of
                 Brin and Page and the HITS algorithm of Kleinberg,
                 score Web pages based on the principal eigenvector (or
                 singular vector) of a particular non-negative matrix
                 that captures the hyperlink structure of the Web graph.
                 We propose and test a new methodology that uses
                 multilinear algebra to elicit more information from a
                 higher-order representation of the hyperlink graph. We
                 start by labeling the edges in our graph with the
                 anchor text of the hyperlinks so that the associated
                 linear algebra representation is a sparse, three-way
                 tensor. The first two dimensions of the tensor
                 represent the Web pages while the third dimension adds
                 the anchor text. We then use the rank-1 factors of a
                 multilinear PARAFAC tensor decomposition, which are
                 akin to singular vectors of the SVD, to automatically
                 identify topics in the collection along with the
                 associated authoritative Web pages.",
  acknowledgement = ack-nhfb,
  pagecount =    "8",
}

@InProceedings{Kurland:2005:PHS,
  author =       "Oren Kurland and Lillian Lee",
  editor =       "{ACM}",
  booktitle =    "Proceedings of the 28th annual international ACM SIGIR
                 conference on Research and development in information
                 retrieval",
  title =        "{PageRank} without hyperlinks: structural re-ranking
                 using links induced by language models",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "306--313",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1145/383952.384019",
  ISBN =         "1-59593-034-5",
  ISBN-13 =      "978-1-59593-034-7",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:07 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Inspired by the PageRank and HITS (hubs and
                 authorities) algorithms for Web search, we propose a
                 structural re-ranking approach to ad hoc information
                 retrieval: we reorder the documents in an initially
                 retrieved set by exploiting asymmetric relationships
                 between them. Specifically, we consider generation
                 links, which indicate that the language model induced
                 from one document assigns high probability to the text
                 of another; in doing so, we take care to prevent bias
                 against long documents. We study a number of re-ranking
                 criteria based on measures of centrality in the graphs
                 formed by generation links, and show that integrating
                 centrality into standard language-model-based retrieval
                 is quite effective at improving precision at top
                 ranks.",
  acknowledgement = ack-nhfb,
  keywords =     "authorities; graph-based retrieval; high-accuracy
                 retrieval; HITS; hubs; language modeling; PageRank;
                 social networks; structural re-ranking",
}

@Article{Langville:2005:RPP,
  author =       "Amy N. Langville and Carl D. Meyer",
  title =        "A Reordering for the {PageRank} Problem",
  journal =      j-SIAM-J-SCI-COMP,
  volume =       "27",
  number =       "6",
  pages =        "2112--2120",
  month =        nov,
  year =         "2005",
  CODEN =        "SJOCE3",
  DOI =          "http://dx.doi.org/10.1137/040607551",
  ISSN =         "1064-8275 (print), 1095-7197 (electronic)",
  ISSN-L =       "1064-8275",
  MRclass =      "68U35 (65F30); 65F30 65C40 60J22 65F50",
  MRnumber =     "MR2211442 (2006k:68167)",
  bibdate =      "Tue Jun 27 09:24:29 MDT 2006",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SISC/27/6;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib;
                 http://epubs.siam.org/volume-27/art_60755.html",
  ZMnumber =     "1103.65048",
  abstract =     "We describe a reordering particularly suited to the
                 PageRank problem, which reduces the computation of the
                 PageRank vector to that of solving a much smaller
                 system and then using forward substitution to get the
                 full solution vector. We compare the theoretical rates
                 of convergence of the original PageRank algorithm to
                 that of the new reordered PageRank algorithm, showing
                 that the new algorithm can do no worse than the
                 original algorithm. We present results of an
                 experimental comparison on five datasets, which
                 demonstrate that the reordered PageRank algorithm can
                 provide a speedup of as much as a factor of 6. We also
                 note potential additional benefits that result from the
                 proposed reordering.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Scientific Computing",
  journal-URL =  "http://epubs.siam.org/sisc",
}

@Article{Langville:2005:UMC,
  author =       "Amy N. Langville and Carl D. Meyer",
  title =        "Updating {Markov} Chains with an Eye on {Google}'s
                 {PageRank}",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "27",
  number =       "4",
  pages =        "968--987",
  year =         "2005",
  CODEN =        "SJMAEL",
  DOI =          "http://dx.doi.org/10.1137/040619028",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "An iterative algorithm based on
                 aggregation/disaggregation principles is presented for
                 updating the stationary distribution of a finite
                 homogeneous irreducible Markov chain. The focus is on
                 large-scale problems of the kind that are characterized
                 by Google's PageRank application, but the algorithm is
                 shown to work well in general contexts. The algorithm
                 is flexible in that it allows for changes to the
                 transition probabilities as well as for the creation or
                 deletion of states. In addition to establishing the
                 rate of convergence, it is proven that the algorithm is
                 globally convergent. Results of numerical experiments
                 are presented.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
  keywords =     "aggregation/disaggregation; Google; Markov chains;
                 PageRank; stationary vector; stochastic
                 complementation; updating",
}

@InProceedings{Liu:2005:WIA,
  author =       "Tie-Yan Liu and Wei-Ying Ma",
  title =        "Webpage importance analysis using conditional {Markov}
                 random walk",
  crossref =     "Skowron:2005:PIW",
  pages =        "515--521",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/WI.2005.161",
  bibdate =      "Thu May 06 16:39:05 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Manaskasemsak:2005:EPB,
  author =       "Bundit Manaskasemsak and Arnon Rungsawang",
  booktitle =    "{Proceedings of the 11th International Conference on
                 Parallel and Distributed Systems (2005)}",
  title =        "An efficient partition-based parallel {PageRank}
                 algorithm",
  crossref =     "Barolli:2005:ICP",
  volume =       "1",
  pages =        "257--263 Vol. 1",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/ICPADS.2005.85",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1531136",
  abstract =     "PageRank becomes the most well-known re-ranking
                 technique of the search results. By its iterative
                 computational nature, the computation takes much
                 computing time and resource. Researchers have then
                 devoted much attention in studying an efficient way to
                 compute the PageRank scores of a very large web graph.
                 However, only a few of them focus on large-scale
                 PageRank computation using parallel processing
                 techniques. In this paper, we propose a Partition-based
                 parallel PageRank algorithm that can efficiently run on
                 a low-cost parallel environment like the PC cluster.
                 For comparison, we also study the other two known
                 techniques, as well as propose an analytical discussion
                 concerning I/O and synchronization cost, and memory
                 usage. Experimental results with two web graphs
                 synthesized from the {\tt .TH} domain and the Stanford
                 WebBase project are very promising.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10248",
}

@InProceedings{Martins:2005:GRA,
  author =       "B. Martins and M. J. Silva",
  title =        "A graph-ranking algorithm for geo-referencing
                 documents",
  crossref =     "Han:2005:FII",
  pages =        "??--??",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/ICDM.2005.6",
  bibdate =      "Thu May 06 15:33:58 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  pagecount =    "4",
}

@PhdThesis{Mason:2005:DCP,
  author =       "Kahn Mason",
  title =        "Detecting colluders in {PageRank} finding slow mixing
                 states in a {Markov} chain",
  type =         "Thesis ({Ph.D.})",
  school =       "Stanford University",
  address =      "Stanford, CA, USA",
  pages =        "75",
  year =         "2005",
  ISBN =         "0-542-29567-9",
  ISBN-13 =      "978-0-542-29567-6",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "Order number AAI3187317.",
  URL =          "http://wwwlib.umi.com/dissertations/fullcit/3187317",
  abstract =     "The PageRank algorithm evaluates webpage reputations
                 based on the hyperlinks that connect them. Webpages
                 that collude to boost their reputations significantly
                 distort the resulting rankings. We introduce a measure
                 for assessing the degree to which a set of webpages
                 boosts its reputation. There is no known efficient
                 algorithm that is guaranteed to detect significantly
                 boosted sets when they exist. However, we provide
                 metrics that, under reasonable conditions, are
                 guaranteed to detect a member of a significantly
                 boosted set, if one exists, and address various
                 implementation issues that arise in incorporating these
                 metrics into PageRank.",
  acknowledgement = ack-nhfb,
  advisor =      "Benjamin Van Roy",
}

@InProceedings{Massa:2005:PRU,
  author =       "P. Massa and C. Hayes",
  title =        "{Page-reRank}: using trusted links to re-rank
                 authority",
  crossref =     "Skowron:2005:PIW",
  pages =        "614--617",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/WI.2005.112",
  bibdate =      "Thu May 06 16:22:59 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@InProceedings{McSherry:2005:UAA,
  author =       "Frank McSherry",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceedings of
                 the 14th international conference on World Wide Web",
  title =        "A uniform approach to accelerated {PageRank}
                 computation",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "575--582",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1145/775152.775191",
  ISBN =         "1-59593-046-9",
  ISBN-13 =      "978-1-59593-046-0",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In this note we consider a simple reformulation of the
                 traditional power iteration algorithm for computing the
                 stationary distribution of a Markov chain. Rather than
                 communicate their current probability values to their
                 neighbors at each step, nodes instead communicate only
                 changes in probability value. This reformulation
                 enables a large degree of flexibility in the manner in
                 which nodes update their values, leading to an array of
                 optimizations and features, including faster
                 convergence, efficient incremental updating, and a
                 robust distributed implementation.While the spirit of
                 many of these optimizations appear in previous
                 literature, we observe several cases where this
                 unification simplifies previous work, removing
                 technical complications and extending their range of
                 applicability. We implement and measure the performance
                 of several optimizations on a sizable (34M node) web
                 subgraph, seeing significant composite performance
                 gains, especially for the case of incremental
                 recomputation after changes to the web graph.",
  acknowledgement = ack-nhfb,
  keywords =     "link analysis; PageRank; random walks; web graph",
}

@InProceedings{Padmanabhan:2005:WWI,
  author =       "D. Padmanabhan and P. Desikan and J. Srivastava and K.
                 Riaz",
  title =        "{WICER}: a weighted inter-cluster edge ranking for
                 clustered graphs",
  crossref =     "Skowron:2005:PIW",
  pages =        "522--528",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/WI.2005.166",
  bibdate =      "Thu May 06 16:37:10 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@InProceedings{Rungsawang:2005:PBP,
  author =       "Arnon Rungsawang and Bundit Manaskasemsak",
  booktitle =    "{ICITA 2005: Third International Conference on
                 Information Technology and Applications}",
  title =        "Partition-Based Parallel {PageRank} Algorithm",
  crossref =     "He:2005:TIC",
  volume =       "2",
  pages =        "57--62",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/ICITA.2005.207",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1488928",
  abstract =     "A re-ranking technique, called 'PageRank' brings a
                 successful story behind the Google search engine. Many
                 studies focus on finding an efficient way to compute
                 the PageRank scores of a large web graph. Researchers
                 propose to compute them sequentially by reducing the
                 I/O cost of disk access, improving the convergence
                 rate, or even employing Peer-2-Peer architecture, etc.
                 However, only a few concentrate on computation using
                 parallel processing techniques. In this paper, we
                 propose a Partition-based parallel PageRank algorithm
                 that can be efficiently run on a low-cost parallel
                 environment like PC cluster. For comparison, we also
                 study other two well-known PageRank techniques, and
                 provide an analytical discussion of their performance
                 in terms of I/O and synchronization cost, as well as
                 memory usage. Experimental results show a promising
                 improvement on a large artificial web graph synthesized
                 from the {\tt .TH} domain.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9966",
}

@Article{Serra-Capizzano:2005:JCF,
  author =       "Stefano Serra-Capizzano",
  title =        "{Jordan} canonical form of the {Google} matrix: a
                 potential contribution to the {PageRank} computation",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "27",
  number =       "2",
  pages =        "305--312",
  month =        apr,
  year =         "2005",
  CODEN =        "SJMAEL",
  DOI =          "http://dx.doi.org/10.1137/S0895479804441407",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  MRclass =      "15A18 (15A21)",
  MRnumber =     "MR2179674 (2006g:15019)",
  bibdate =      "Thu Dec 29 16:33:54 MST 2005",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SIMAX/27/2;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "See comments \cite{Wu:2008:CJC}.",
  URL =          "http://www.math.utah.edu/pub/tex/bib/siamjmatanaappl.bib;
                 http://epubs.siam.org/sam-bin/dbq/article/44140",
  ZMnumber =     "1103.65051",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
}

@InProceedings{Tarau:2005:SDE,
  author =       "Paul Tarau and Rada Mihalcea and Elizabeth Figa",
  editor =       "{ACM}",
  booktitle =    "Proceedings of the 2005 ACM Symposium on Applied
                 computing",
  title =        "Semantic document engineering with {WordNet} and
                 {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "782--786",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.3115/981658.981684",
  ISBN =         "1-58113-964-0",
  ISBN-13 =      "978-1-58113-964-8",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:04 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "This paper describes Natural Language Processing
                 techniques for document engineering in combination with
                 graph algorithms and statistical methods. Google's
                 PageRank and similar fast-converging recursive graph
                 algorithms have provided practical means to statically
                 rank vertices of large graphs like the World Wide Web.
                 By combining a fast Java-based PageRank implementation
                 with a Prolog base inferential layer, running on top of
                 an optimized WordNet graph, we describe applications to
                 word sense disambiguation and evaluate their accuracy
                 on standard benchmarks.",
  acknowledgement = ack-nhfb,
  keywords =     "logic programming; natural language processing;
                 PageRank-style graph algorithms; semantics-based
                 document processing; word sense disambiguation;
                 WordNet",
}

@InProceedings{Tummarello:2005:SAH,
  author =       "G. Tummarello and C. Morbidoni and P. Puliti and F.
                 Piazza",
  title =        "Semantic audio hyperlinking: a multimedia-semantic
                 {Web} scenario",
  crossref =     "Nesi:2005:FIC",
  pages =        "??--??",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/AXMEDIS.2005.45",
  bibdate =      "Thu May 06 15:54:55 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Wang:2005:DP,
  author =       "Xuanhui Wang and Azadeh Shakery and Tao Tao",
  editor =       "{ACM}",
  booktitle =    "Annual ACM Conference on Research and Development in
                 Information Retrieval Proceedings of the 28th annual
                 international ACM SIGIR conference on Research and
                 development in information retrieval",
  title =        "{Dirichlet PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "661--662",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1145/383952.384019",
  ISBN =         "1-59593-034-5",
  ISBN-13 =      "978-1-59593-034-7",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:11 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "PageRank has been known to be a successful algorithm
                 in ranking web sources. In order to avoid the rank sink
                 problem, PageRank assumes that a surfer, being in a
                 page, jumps to a random page with a certain
                 probability. In the standard PageRank algorithm, the
                 jumping probabilities are assumed to be the same for
                 all the pages, regardless of the page properties. This
                 is not the case in the real world, since presumably a
                 surfer would more likely follow the out-links of a
                 high-quality hub page than follow the links of a
                 low-quality one. In this poster, we propose a novel
                 algorithm `Dirichlet PageRank' to address this problem
                 by adapting flexible jumping probabilities based on the
                 number of out-links in a page. Empirical results on
                 TREC data show that our method outperforms the standard
                 PageRank algorithm.",
  acknowledgement = ack-nhfb,
}

@Article{Weingart:2005:IBU,
  author =       "Peter Weingart",
  title =        "Impact of bibliometrics upon the science system:
                 Inadvertent consequences?",
  journal =      j-SCIENTOMETRICS,
  volume =       "62",
  number =       "1",
  pages =        "117--131",
  month =        "????",
  year =         "2005",
  CODEN =        "SCNTDX",
  DOI =          "http://dx.doi.org/10.1007/s11192-005-0007-7",
  ISSN =         "0138-9130 (print), 1588-2861 (electronic)",
  ISSN-L =       "0138-9130",
  bibdate =      "Thu Jun 02 08:35:18 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Ranking of research institutions by bibliometric
                 methods is an improper tool for research performance
                 evaluation, even at the level of large institutions.
                 The problem, however, is not the ranking as such. The
                 indicators used for ranking are often not advanced
                 enough, and this situation is part of the broader
                 problem of the application of insufficiently developed
                 bibliometric indicators used by persons who do not have
                 clear competence and experience in the field of
                 quantitative studies of science. After a brief overview
                 of the basic elements of bibliometric analysis, I
                 discuss the major technical and methodological problems
                 in the application of publication and citation data in
                 the context of evaluation. Then I contend that the core
                 of the problem lies not necessarily at the side of the
                 data producer. Quite often persons responsible for
                 research performance evaluation, for instance
                 scientists themselves in their role as head of
                 institutions and departments, science administrators at
                 the government level and other policy makers show an
                 attitude that encourages `quick and dirty' bibliometric
                 analyses whereas better quality is available. Finally,
                 the necessary conditions for a successful application
                 of advanced bibliometric indicators as support tool for
                 peer review are discussed.",
  acknowledgement = ack-nhfb,
  fjournal =     "Scientometrics",
}

@InProceedings{Wu:2005:ULM,
  author =       "Jie Wu and K. Aberer",
  title =        "Using a Layered {Markov} Model for Distributed {Web}
                 Ranking Computation",
  crossref =     "IEEE:2005:ICD",
  pages =        "533--542",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/ICDCS.2005.84",
  bibdate =      "Thu May 06 15:11:10 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Yang:2005:RMW,
  author =       "Christopher C. Yang and K. Y. Chan",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Special
                 interest tracks and posters of the 14th international
                 conference on World Wide Web",
  title =        "Retrieving multimedia {Web} objects based on
                 {PageRank} algorithm",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "906--907",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1145/511446.511454",
  ISBN =         "1-59593-051-5",
  ISBN-13 =      "978-1-59593-051-4",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:11 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Hyperlink analysis has been widely investigated to
                 support the retrieval of Web documents in Internet
                 search engines. It has been proven that the hyperlink
                 analysis significantly improves the relevance of the
                 search results and these techniques have been adopted
                 in many commercial search engines, e.g. Google.
                 However, hyperlink analysis is mostly utilized in the
                 ranking mechanism of Web pages only but not including
                 other multimedia objects, such as images and video. In
                 this project, we propose a modified Multimedia PageRank
                 algorithm to support the searching of multimedia
                 objects in the Web.",
  acknowledgement = ack-nhfb,
  keywords =     "content based retrieval; HITS; hyperlink analysis;
                 multimedia retrieval; PageRank; web search engines",
}

@InProceedings{Yu:2005:ATD,
  author =       "P. S. Yu and Xin Li and Bing Liu",
  title =        "Adding the temporal dimension to search --- a case
                 study in publication search",
  crossref =     "Skowron:2005:PIW",
  pages =        "543--549",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/WI.2005.21",
  bibdate =      "Thu May 06 16:18:03 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Zhang:2005:CBH,
  author =       "Junlin Zhang and Le Sun and Quan Zhou",
  title =        "A cue-based hub-authority approach for multi-document
                 text summarization",
  crossref =     "IEEE:2005:PII",
  pages =        "642--645",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1109/NLPKE.2005.1598815",
  bibdate =      "Thu May 06 15:31:40 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@InProceedings{Zhu:2005:DPC,
  author =       "Yangbo Zhu and Shaozhi Ye and Xing Li",
  editor =       "{ACM}",
  booktitle =    "Conference on Information and Knowledge Management
                 Proceedings of the 14th ACM international conference on
                 Information and knowledge management",
  title =        "Distributed {PageRank} computation based on iterative
                 aggregation-disaggregation methods",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "578--585",
  year =         "2005",
  DOI =          "http://dx.doi.org/10.1145/1099554.1099705",
  ISBN =         "1-59593-140-6",
  ISBN-13 =      "978-1-59593-140-5",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "PageRank has been widely used as a major factor in
                 search engine ranking systems. However, global link
                 graph information is required when computing PageRank,
                 which causes prohibitive communication cost to achieve
                 accurate results in distributed solution. In this
                 paper, we propose a distributed PageRank computation
                 algorithm based on iterative aggregation-disaggregation
                 (IAD) method with Block Jacobi smoothing. The basic
                 idea is divide-and-conquer. We treat each web site as a
                 node to explore the block structure of hyperlinks.
                 Local PageRank is computed by each node itself and then
                 updated with a low communication cost with a
                 coordinator. We prove the global convergence of the
                 Block Jacobi method and then analyze the communication
                 overhead and major advantages of our algorithm.
                 Experiments on three real web graphs show that our
                 method converges 5-7 times faster than the traditional
                 Power method. We believe our work provides an efficient
                 and practical distributed solution for PageRank on
                 large scale Web graphs.",
  acknowledgement = ack-nhfb,
  keywords =     "block Jacobi; distributed search engines; iterative
                 aggregation-disaggregation; PageRank",
}

@InProceedings{Akian:2006:PMS,
  author =       "Marianne Akian and St{\'e}phane Gaubert and Laure
                 Ninove",
  editor =       "Christian Commault and Nicolas Marchand",
  booktitle =    "{Positive systems: proceedings of the second
                 Multidisciplinary International Symposium on Positive
                 Systems: Theory and Applications (POSTA 06), Grenoble,
                 France, Aug. 30-31, Sept. 1, 2006}",
  title =        "The {$T$-PageRank}: a model of self-validating effects
                 of web surfing",
  volume =       "341",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "239--246",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1007/3-540-34774-7_31",
  ISBN =         "3-540-34774-7, 3-540-34771-2",
  ISBN-13 =      "978-3-540-34774-3, 978-3-540-34771-2",
  LCCN =         "QA402 .M86 2006",
  MRclass =      "68U35",
  MRnumber =     "MR2250261",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCIS,
  ZMnumber =     "1121.68007",
  acknowledgement = ack-nhfb,
  bookpages =    "xiv + 448",
}

@InProceedings{Ali:2006:ACC,
  author =       "R. Ali and M. M. S. Beg",
  title =        "Aggregating Content and Connectivity based Techniques
                 for Measure of {Web} Search Quality",
  crossref =     "IEEE:2006:AAC",
  pages =        "44--49",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/ADCOM.2006.4289853",
  bibdate =      "Thu May 06 15:35:59 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Andersen:2006:LGP,
  author =       "Reid Andersen and Fan Chung and Kevin Lang",
  booktitle =    "{FOCS '06: 47th Annual IEEE Symposium on Foundations
                 of Computer Science (2006)}",
  title =        "Local Graph Partitioning using {PageRank} Vectors",
  crossref =     "IEEE:2006:AIS",
  pages =        "475--486",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/FOCS.2006.44",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4031383",
  abstract =     "A local graph partitioning algorithm finds a cut near
                 a specified starting vertex, with a running time that
                 depends largely on the size of the small side of the
                 cut, rather than the size of the input graph. In this
                 paper, we present a local partitioning algorithm using
                 a variation of PageRank with a specified starting
                 distribution. We derive a mixing result for PageRank
                 vectors similar to that for random walks, and show that
                 the ordering of the vertices produced by a PageRank
                 vector reveals a cut with small conductance. In
                 particular, we show that for any set C with conductance
                 \Phiand volume k, a PageRank vector with a certain
                 starting distribution can be used to produce a set with
                 conductance O\left( {\sqrt {\Phi \log k} } \right). We
                 present an improved algorithm for computing approximate
                 PageRank vectors, which allows us to find such a set in
                 time proportional to its size. In particular, we can
                 find a cut with conductance at most \not o , whose
                 small side has volume at least 2b, in time O\left( {2^b
                 \log ^2 m/\not o^2 } \right) where m is the number of
                 edges in the graph. By combining small sets found by
                 this local partitioning algorithm, we obtain a cut with
                 conductance \not o and approximately optimal balance in
                 time O\left( {m\log ^4 m/\not o^2 } \right).",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4031329",
}

@Article{Avrachenkov:2006:ENL,
  author =       "Konstantin Avrachenkov and Nelly Litvak",
  title =        "The effect of new links on {Google} {PageRank}",
  journal =      j-STOCH-MODELS,
  volume =       "22",
  number =       "2",
  pages =        "319--331",
  year =         "2006",
  CODEN =        "CSSME8",
  DOI =          "http://dx.doi.org/10.1080/15326340600649052",
  ISSN =         "1532-6349",
  MRclass =      "68U35 (90B18 91D30)",
  MRnumber =     "MR2220968 (2007f:68227)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1094.68005",
  acknowledgement = ack-nhfb,
  fjournal =     "Stochastic Models",
}

@Article{Avrachenkov:2006:PSF,
  author =       "Konstantin Avrachenkov and Dmitri Lebedev",
  title =        "{PageRank} of scale-free growing networks",
  journal =      j-INTERNET-MATH,
  volume =       "3",
  number =       "2",
  pages =        "207--231",
  year =         "2006",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "05C80 (68M10 68R10 68U35)",
  MRnumber =     "MR2321830 (2008c:05162)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1204906139",
  ZMnumber =     "1122.68406",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@InProceedings{Baeza-Yates:2006:GPD,
  author =       "Ricardo Baeza-Yates and Paolo Boldi and Carlos
                 Castillo",
  editor =       "{ACM}",
  booktitle =    "Annual ACM Conference on Research and Development in
                 Information Retrieval Proceedings of the 29th annual
                 international ACM SIGIR conference on Research and
                 development in information retrieval",
  title =        "Generalizing {PageRank}: damping functions for
                 link-based ranking algorithms",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "308--315",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1007/s10791-005-6993-5",
  ISBN =         "1-59593-369-7",
  ISBN-13 =      "978-1-59593-369-0",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "This paper introduces a family of link-based ranking
                 algorithms that propagate page importance through
                 links. In these algorithms there is a damping function
                 that decreases with distance, so a direct link implies
                 more endorsement than a link through a long path.
                 PageRank is the most widely known ranking function of
                 this family.The main objective of this paper is to
                 determine whether this family of ranking techniques has
                 some interest per se, and how different choices for the
                 damping function impact on rank quality and on
                 convergence speed. Even though our results suggest that
                 PageRank can be approximated with other simpler forms
                 of rankings that may be computed more efficiently, our
                 focus is of more speculative nature, in that it aims at
                 separating the kernel of PageRank, that is, link-based
                 importance propagation, from the way propagation decays
                 over paths.We focus on three damping functions, having
                 linear, exponential, and hyperbolic decay on the
                 lengths of the paths. The exponential decay corresponds
                 to PageRank, and the other functions are new. Our
                 presentation includes algorithms, analysis, comparisons
                 and experiments that study their behavior under
                 different parameters in real Web graph data.Among other
                 results, we show how to calculate a linear
                 approximation that induces a page ordering that is
                 almost identical to PageRank's using a fixed small
                 number of iterations; comparisons were performed using
                 Kendall's $\tau$ on large domain datasets.",
  acknowledgement = ack-nhfb,
  keywords =     "link analysis; link-based ranking; web graphs",
}

@InProceedings{Bansal:2006:ADC,
  author =       "T. Bansal and P. Ghanshani and R. C. Joshi",
  title =        "An Application Dependent Communication Protocol for
                 Wireless Sensor Networks",
  crossref =     "IEEE:2006:IIM",
  pages =        "120--120",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/ICNICONSMCL.2006.46",
  bibdate =      "Thu May 06 16:24:09 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@Article{Bao:2006:LPD,
  author =       "Ying Bao and Yong Liu",
  title =        "Limit of {PageRank} with damping factor",
  journal =      j-DYN-CONTIN-DISCR-IMPULS-B,
  volume =       "13",
  number =       "3-4",
  pages =        "497--504",
  year =         "2006",
  CODEN =        "DCDIS4",
  ISSN =         "1492-8760",
  MRclass =      "68U35 (60J27 68P20 68W40)",
  MRnumber =     "MR2208501",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1100.60040",
  acknowledgement = ack-nhfb,
  fjournal =     "Dynamics of Continuous, Discrete \& Impulsive Systems.
                 Series B. Applications \& Algorithms",
}

@InProceedings{Becchetti:2006:DPF,
  author =       "Luca Becchetti and Carlos Castillo",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceedings of
                 the 15th international conference on World Wide Web",
  title =        "The distribution of {PageRank} follows a power-law
                 only for particular values of the damping factor",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "941--942",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1016/S1389-1286(00)00063-3",
  ISBN =         "1-59593-323-9",
  ISBN-13 =      "978-1-59593-323-2",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We show that the empirical distribution of the
                 PageRank values in a large set of Web pages does not
                 follow a power-law except for some particular choices
                 of the damping factor. We argue that for a graph with
                 an in-degree distribution following a power-law with
                 exponent between 2.1 and 2.2, choosing a damping factor
                 around 0.85 for PageRank yields a power-law
                 distribution of its values. We suggest that power-law
                 distributions of PageRank in Web graphs have been
                 observed because the typical damping factor used in
                 practice is between 0.85 and 0.90.",
  acknowledgement = ack-nhfb,
  keywords =     "pagerank distribution; web graph",
}

@Article{Berkhin:2006:BCA,
  author =       "Pavel Berkhin",
  title =        "Bookmark-coloring algorithm for personalized
                 {PageRank} computing",
  journal =      j-INTERNET-MATH,
  volume =       "3",
  number =       "1",
  pages =        "41--62",
  year =         "2006",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (68M10 68R10); 68P10 68M10 68P20 68W05",
  MRnumber =     "MR2283883 (2007k:68134)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1175266367",
  ZMnumber =     "1113.68375",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@Article{Boldi:2006:GFG,
  author =       "Paolo Boldi and Violetta Lonati and Massimo Santini
                 and Sebastiano Vigna",
  title =        "Graph fibrations, graph isomorphism, and {PageRank}",
  journal =      j-INFORM-THEOR-APPL,
  volume =       "40",
  number =       "2",
  pages =        "227--253",
  year =         "2006",
  CODEN =        "RSITD7, RITAE4",
  DOI =          "http://dx.doi.org/10.1051/ita:2006004",
  ISSN =         "0988-3754 (print), 1290-385X (electronic)",
  ISSN-L =       "0988-3754",
  MRclass =      "68U35 (05C60 60J10 60J20 68R10 94C15)",
  MRnumber =     "MR2252637 (2007h:68204)",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1112.68002",
  acknowledgement = ack-nhfb,
  fjournal =     "Theoretical Informatics and Applications. Informatique
                 Th\'eorique et Applications",
}

@Article{Brezinski:2006:PVP,
  author =       "Claude Brezinski and Michela Redivo-Zaglia",
  title =        "The {PageRank} vector: properties, computation,
                 approximation, and acceleration",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "28",
  number =       "2",
  pages =        "551--575",
  year =         "2006",
  CODEN =        "SJMAEL",
  DOI =          "http://dx.doi.org/10.1137/050626612",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  MRclass =      "68U35 (65F15)",
  MRnumber =     "MR2255342 (2007h:68205)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1116.65042",
  abstract =     "An important problem in Web search is determining the
                 importance of each page. After introducing the main
                 characteristics of this problem, we will see that, from
                 the mathematical point of view, it could be solved by
                 computing the left principal eigenvector (the PageRank
                 vector) of a matrix related to the structure of the Web
                 by using the power method. We will give expressions of
                 the PageRank vector and study the mathematical
                 properties of the power method. Various Pad{\'e}-style
                 approximations of the PageRank vector will be given.
                 Since the convergence of the power method is slow, it
                 has to be accelerated. This problem will be discussed.
                 Recently, several acceleration methods were proposed.
                 We will give a theoretical justification for these
                 methods. In particular, we will generalize the recently
                 proposed Quadratic Extrapolation, and we interpret it
                 on the basis of the method of moments of Vorobyev, and
                 as a Krylov subspace method. Acceleration results are
                 given for the various epsilon -algorithms, and for the
                 E -algorithm. Other algorithms for this problem are
                 also discussed.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
}

@Article{Brinkmeier:2006:PR,
  author =       "Michael Brinkmeier",
  title =        "{PageRank} revisited",
  journal =      j-TOIT,
  volume =       "6",
  number =       "3",
  pages =        "282--301",
  month =        aug,
  year =         "2006",
  CODEN =        "????",
  DOI =          "http://doi.acm.org/10.1145/1151087.1151090",
  ISSN =         "1533-5399 (print), 1557-6051 (electronic)",
  ISSN-L =       "1533-5399",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.acm.org/pubs/contents/journals/toit/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/toit.bib",
  abstract =     "PageRank, one part of the search engine Google, is one
                 of the most prominent link-based rankings of documents
                 in the World Wide Web. Usually it is described as a
                 Markov chain modeling a specific random surfer. In this
                 article, an alternative representation as a power
                 series is given. Nonetheless, it is possible to
                 interpret the values as probabilities in a random
                 surfer setting, differing from the usual one. Using the
                 new description we restate and extend some results
                 concerning the convergence of the standard iteration
                 used for PageRank. Furthermore we take a closer look at
                 sinks and sources, leading to some suggestions for
                 faster implementations.",
  acknowledgement = ack-nhfb,
  fjournal =     "ACM Transactions on Internet Technology (TOIT)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J780",
  keywords =     "Dynamical update; link-analysis; Markov chain;
                 Pagerank; personalization; random surfer; ranking
                 algorithm; Web graph; Web page scoring; Web search;
                 World Wide Web",
}

@Article{Broder:2006:EPA,
  author =       "A. Z. Broder and R. Lempel and F. Maghoul and J.
                 Pedersen",
  title =        "Efficient {PageRank} approximation via graph
                 aggregation",
  journal =      j-INF-RETR,
  volume =       "9",
  number =       "2",
  pages =        "123--138",
  month =        mar,
  year =         "2006",
  CODEN =        "IFRTFY",
  DOI =          "http://dx.doi.org/10.1145/775152.775203",
  ISSN =         "1386-4564 (print), 1573-7659 (electronic)",
  ISSN-L =       "1386-4564",
  bibdate =      "Sat May 8 18:33:07 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We present a framework for approximating random-walk
                 based probability distributions over Web pages using
                 graph aggregation. The basic idea is to partition the
                 graph into classes of quasi-equivalent vertices, to
                 project the page-based random walk to be approximated
                 onto those classes, and to compute the stationary
                 probability distribution of the resulting class-based
                 random walk. From this distribution we can quickly
                 reconstruct a distribution on pages. In particular, our
                 framework can approximate the well-known PageRank
                 distribution by setting the classes according to the
                 set of pages on each Web host. \par

                 We experimented on a Web-graph containing over 1.4
                 billion pages and over 6.6 billion links from a crawl
                 of the Web conducted by AltaVista in September 2003. We
                 were able to produce a ranking that has Spearman
                 rank-order correlation of 0.95 with respect to
                 PageRank. The clock time required by a simplistic
                 implementation of our method was less than half the
                 time required by a highly optimized implementation of
                 PageRank, implying that larger speedup factors are
                 probably possible.",
  acknowledgement = ack-nhfb,
  fjournal =     "Information Retrieval",
  keywords =     "Citation and link analysis; Web IR",
}

@Article{Bryan:2006:ELA,
  author =       "Kurt Bryan and Tanya Leise",
  title =        "The \$25,000,000,000 Eigenvector: The Linear Algebra
                 behind {Google}",
  journal =      j-SIAM-REVIEW,
  volume =       "48",
  number =       "3",
  pages =        "569--581",
  month =        "????",
  year =         "2006",
  CODEN =        "SIREAD",
  DOI =          "http://dx.doi.org/10.1137/050623280",
  ISSN =         "0036-1445 (print), 1095-7200 (electronic)",
  ISSN-L =       "0036-1445",
  bibdate =      "Tue Dec 2 17:02:29 MST 2008",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SIREV/48/3;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/siamreview.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Review",
  journal-URL =  "http://epubs.siam.org/sirev",
  keywords =     "PageRank; singular-value decomposition; SVD",
}

@InProceedings{Chongsuntornsri:2006:ATT,
  author =       "Aekkasit Chongsuntornsri and Ohm Sornil",
  booktitle =    "{ISCIT '06: International Symposium on Communications
                 and Information Technologies (2006)}",
  title =        "An Automatic {Thai} Text Summarization Using Topic
                 Sensitive {PageRank}",
  crossref =     "IEEE:2006:CIT",
  pages =        "547--552",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/ISCIT.2006.340009",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4141445",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4141327",
}

@InProceedings{Davis:2006:EGP,
  author =       "Jason V. Davis and Inderjit S. Dhillon",
  editor =       "{ACM}",
  booktitle =    "International Conference on Knowledge Discovery and
                 Data Mining Proceedings of the 12th ACM SIGKDD
                 international conference on Knowledge discovery and
                 data mining",
  title =        "Estimating the global {PageRank} of {Web}
                 communities",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "116--125",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1145/1099554.1099583",
  ISBN =         "1-59593-339-5",
  ISBN-13 =      "978-1-59593-339-3",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Localized search engines are small-scale systems that
                 index a particular community on the web. They offer
                 several benefits over their large-scale counterparts in
                 that they are relatively inexpensive to build, and can
                 provide more precise and complete search capability
                 over their relevant domains. One disadvantage such
                 systems have over large-scale search engines is the
                 lack of global PageRank values. Such information is
                 needed to assess the value of pages in the localized
                 search domain within the context of the web as a whole.
                 In this paper, we present well-motivated algorithms to
                 estimate the global PageRank values of a local domain.
                 The algorithms are all highly scalable in that, given a
                 local domain of size n, they use O(n) resources that
                 include computation time, bandwidth, and storage. We
                 test our methods across a variety of localized domains,
                 including site-specific domains and topic-specific
                 domains. We demonstrate that by crawling as few as n or
                 2n additional pages, our methods can give excellent
                 global PageRank estimates.",
  acknowledgement = ack-nhfb,
  keywords =     "Markov chain; page rank; stochastic complementation",
}

@InProceedings{DeLong:2006:CAR,
  author =       "Colin DeLong and Sandeep Mane and Jaideep Srivastava",
  title =        "Concept-Aware Ranking: Teaching an Old Graph New
                 Moves",
  crossref =     "Clifton:2006:SIC",
  pages =        "80--88",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/ICDMW.2006.49",
  bibdate =      "Thu May 06 15:42:10 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Desikan:2006:DCA,
  author =       "Prasanna Kumar Desikan and Nishith Pathak and Jaideep
                 Srivastava and Vipin Kumar",
  editor =       "{ACM}",
  booktitle =    "{Proceedings of the 6th international conference on
                 Web engineering}",
  title =        "Divide and conquer approach for efficient {PageRank}
                 computation",
  volume =       "263",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "233--240",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1145/988672.988714",
  ISBN =         "1-59593-352-2",
  ISBN-13 =      "978-1-59593-352-2",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "PageRank is a popular ranking metric for large graphs
                 such as the World Wide Web. Current research techniques
                 for improving computational efficiency of PageRank have
                 focused on improving the I/O cost, convergence and
                 parallelizing the computation process. In this paper,
                 we propose a divide and conquer strategy for efficient
                 computation of PageRank. The strategy is different from
                 contemporary improvements in that it can be combined
                 with any existing enhancements to PageRank, giving way
                 to an entire class of more efficient algorithms. We
                 present a novel graph-partitioning technique for
                 dividing the graph into subgraphs, on which computation
                 can be performed independently. This approach has two
                 significant benefits. Firstly, since the approach
                 focuses on work-reduction, it can be combined with any
                 existing enhancements to PageRank. Secondly, the
                 proposed approach leads naturally into developing an
                 incremental approach for computation of such ranking
                 metrics given that these large graphs evolve over a
                 period of time. The partitioning technique is both
                 lossless and independent of the type (variant) of
                 PageRank computation algorithm used. The experimental
                 results for a static single graph (graph at a single
                 time instance) as well as for the incremental
                 computation in case of evolving graphs, illustrate the
                 utility of our novel partitioning approach. The
                 proposed approach can also be applied for the
                 computation of any other metric based on first order
                 Markov chain model.",
  acknowledgement = ack-nhfb,
  keywords =     "efficient computation; graph partitioning; PageRank;
                 ranking measures",
}

@Article{Gleich:2006:APP,
  author =       "David Gleich and Marzia Polito",
  title =        "Approximating personalized {PageRank} with minimal use
                 of web graph data",
  journal =      j-INTERNET-MATH,
  volume =       "3",
  number =       "3",
  pages =        "257--294",
  year =         "2006",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (05C85 05C90 68M10 68R10)",
  MRnumber =     "MR2372544 (2008m:68217)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:02 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1204906158",
  ZMnumber =     "1147.68350",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@Article{Golub:2006:ATA,
  author =       "G. H. Golub and C. Greif",
  title =        "An {Arnoldi}-type algorithm for computing {PageRank}",
  journal =      j-BIT,
  volume =       "46",
  number =       "4",
  pages =        "759--771",
  year =         "2006",
  CODEN =        "BITTEL, NBITAB",
  DOI =          "http://dx.doi.org/10.1007/s10543-006-0091-y",
  ISSN =         "0006-3835 (print), 1572-9125 (electronic)",
  ISSN-L =       "0006-3835",
  MRclass =      "65F15",
  MRnumber =     "MR2285207 (2008a:65073)",
  MRreviewer =   "Jan Mandel",
  bibdate =      "Wed May 5 19:28:02 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "BIT. Numerical Mathematics",
  journal-URL =  "http://link.springer.com/journal/10543",
}

@InProceedings{Hamid:2006:RDU,
  author =       "Noorisyam Hamid and Fazilah Haron and Chan Huah Yong",
  title =        "Resource Discovery Using {PageRank} Technique in Grid
                 Environment",
  crossref =     "Turner:2006:SII",
  volume =       "1",
  pages =        "135--140",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/CCGRID.2006.87",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1630807",
  abstract =     "The grid deals with large scale and ever-expanding
                 environment which contains million of users and
                 resources. For this reason, resource selection has been
                 a challenging task especially in meeting user's demand
                 for a quality of service (QoS). A quality of service is
                 the ability to serve a job by providing quality and
                 reliable resource in fulfilling the user's need.
                 Quality and reliable resource selections naturally
                 yield excellent and quality results. The background of
                 the users and where the resource belongs to are
                 important in determining the quality of a resource.
                 This paper concerns with efficient and quality-based
                 resource discovery using Condor ClassAd and PageRank
                 technique in order to achieve a quality resource
                 matching. The paper discusses how quality of users and
                 resources are determined and considered in the
                 discovery process prior to allocating jobs to
                 resources.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10856",
}

@InProceedings{Huang:2006:TPA,
  author =       "Decai Huang and Huachun Qi and Yuan Yuan and Yue-feng
                 Zheng",
  booktitle =    "{WCICA 2006: The Sixth World Congress on Intelligent
                 Control and Automation}",
  title =        "{TC-{PageRank}} Algorithm Based on Topic Correlation",
  crossref =     "IEEE:2006:WSW",
  volume =       "2",
  pages =        "5943--5946",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/WCICA.2006.1714219",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1714219",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11210",
}

@Article{Ipsen:2006:CAP,
  author =       "Ilse C. F. Ipsen and Steve Kirkland",
  title =        "Convergence analysis of a {PageRank} updating
                 algorithm by {Langville} and {Meyer}",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "27",
  number =       "4",
  pages =        "952--967",
  year =         "2006",
  CODEN =        "SJMAEL",
  DOI =          "http://dx.doi.org/10.1137/S0895479804439808",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  MRclass =      "65F30 (15A51 68U35); 65F15 65F10 15A18 15A42 65C40
                 15A51 68P10 60J22",
  MRnumber =     "MR2205606 (2006i:65070)",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1108.65030",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
}

@Article{Ipsen:2006:MPA,
  author =       "Ilse C. F. Ipsen and Rebecca S. Wills",
  title =        "Mathematical properties and analysis of {Google}'s
                 {PageRank}",
  journal =      "Bol. Soc. Esp. Mat. Apl. S$\vec{\rm e}$MA",
  volume =       "34",
  pages =        "191--196",
  year =         "2006",
  CODEN =        "????",
  ISSN =         "1575-9822",
  MRclass =      "65F15 (15A51)",
  MRnumber =     "MR2296216",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Bolet\'\i n de la Sociedad Espa\~nola de Matem\'atica
                 Aplicada. S$\vec{\rm e}$MA",
}

@InProceedings{Kabutoya:2006:QEL,
  author =       "Yutaka Kabutoya and Takayuki Yumoto and Satoshi Oyama
                 and Keishi Tajima and Katsumi Tanaka",
  booktitle =    "{Proceedings of the 22nd International Conference on
                 Data Engineering Workshops (2006)}",
  title =        "Quality Estimation of Local Contents Based on
                 {PageRank} Values of {Web} Pages",
  crossref =     "Barga:2006:IPI",
  pages =        "x134--x134",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/ICDEW.2006.121",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1623929",
  abstract =     "Recently, it is getting more frequent to search not
                 Web contents but local contents, e.g., by Google
                 Desktop Search. Google succeeded in the Web search
                 because of its PageRank algorithm for the ranking of
                 the search results. PageRank estimates the quality of
                 Web pages based on their popularity, which in turn is
                 estimated by the number and the quality of pages
                 referring to them through hyperlinks. This algorithm,
                 however, is not applicable when we search local
                 contents without link structure, such as text data. In
                 this research, we propose a method to estimate the
                 quality of local contents without link structure by
                 using the PageRank values of Web contents similar to
                 them. Based on this estimation, we can rank the desktop
                 search results. Furthermore, this method enables us to
                 search contents across different resources such as Web
                 contents and local contents. In this paper, we applied
                 this method to Web contents, calculated the scores that
                 estimate their quality, and we compare them with their
                 page quality scores by PageRank.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10810",
}

@Article{Kirkland:2006:CES,
  author =       "S. Kirkland",
  title =        "Conditioning of the entries in the stationary vector
                 of a {Google}-type matrix",
  journal =      j-LINEAR-ALGEBRA-APPL,
  volume =       "418",
  number =       "2--3",
  pages =        "665--681",
  day =          "15",
  month =        oct,
  year =         "2006",
  CODEN =        "LAAPAW",
  DOI =          "http://dx.doi.org/10.1016/j.laa.2006.03.007",
  ISSN =         "0024-3795 (print), 1873-1856 (electronic)",
  ISSN-L =       "0024-3795",
  bibdate =      "Wed Mar 30 14:18:57 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.sciencedirect.com/science/journal/00243795",
  acknowledgement = ack-nhfb,
  fjournal =     "Linear Algebra and its Applications",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00243795",
  keywords =     "condition number; PageRank; stationary vector;
                 stochastic matrix",
}

@InProceedings{Kozakiewicz:2006:TLA,
  author =       "A. Kozakiewicz and A. Karbowskr",
  title =        "A Two-Level Approach to Building a Campus Grid",
  crossref =     "IEEE:2006:ISP",
  pages =        "121--126",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/PARELEC.2006.11",
  bibdate =      "Thu May 06 15:58:28 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@Book{Langville:2006:GPB,
  author =       "Amy N. Langville and Carl D. (Carl Dean) Meyer",
  title =        "{Google}'s {PageRank} and beyond: the science of
                 search engine rankings",
  publisher =    pub-PRINCETON,
  address =      pub-PRINCETON:adr,
  pages =        "x + 224",
  year =         "2006",
  ISBN =         "0-691-12202-4 (hardcover)",
  ISBN-13 =      "978-0-691-12202-1 (hardcover)",
  LCCN =         "TK5105.885.G66 L36 2006",
  MRclass =      "68-02 (00-01 00A05 15A18 68U35)",
  MRnumber =     "MR2262054 (2007h:68002)",
  MRreviewer =   "Jiu Ding",
  bibdate =      "Fri Oct 23 16:04:57 MDT 2009",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  URL =          "http://www.math.utah.edu/pub/tex/bib/master.bib;
                 http://www.loc.gov/catdir/enhancements/fy0654/2005938841-b.html;
                 http://www.loc.gov/catdir/enhancements/fy0654/2005938841-d.html;
                 http://www.loc.gov/catdir/enhancements/fy0668/2005938841-t.html",
  ZMnumber =     "1104.68042",
  acknowledgement = ack-nhfb,
  libnote =      "Not in my library.",
  subject =      "Google; Web search engines; Web sites; Ratings;
                 Mathematics; Internet searching; World Wide Web;
                 Subject access",
}

@Article{Langville:2006:UMC,
  author =       "Amy N. Langville and Carl D. Meyer",
  title =        "Updating {Markov} chains with an eye on {Google}'s
                 {PageRank}",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "27",
  number =       "4",
  pages =        "968--987",
  year =         "2006",
  CODEN =        "SJMAEL",
  DOI =          "http://dx.doi.org/10.1137/040619028",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  MRclass =      "60J10 (65C40 68P20 68U35); 60J10 65C40 15A51 65F10
                 65F15 65F30 65F50 68P20 68P10 15A99 15-04 15A18 15A06",
  MRnumber =     "MR2205607",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1098.60073",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
}

@InProceedings{Lin:2006:PNL,
  author =       "Zhenjiang Lin and I. King and M. R. Lyu",
  title =        "{PageSim}: {A} Novel Link-Based Similarity Measure for
                 the {World Wide Web}",
  crossref =     "Nishida:2006:IWA",
  pages =        "687--693",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/WI.2006.127",
  bibdate =      "Thu May 06 16:01:07 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InCollection{MadriddelaVega:2006:NLA,
  author =       "Humberto {Madrid de la Vega} and Valia Guerra Ones and
                 Marisol Flores Garrido",
  booktitle =    "{Papers of the Mexican Mathematical Society
                 (Spanish)}",
  title =        "The numerical linear algebra of {Google}'s
                 {PageRank}",
  volume =       "36",
  publisher =    "Soc. Mat. Mexicana",
  address =      "M\'exico",
  pages =        "33--52",
  year =         "2006",
  ISBN =         "????",
  ISBN-13 =      "????",
  MRclass =      "65F15; 68P10 68M10 65F10 65F15 65F30 65F50",
  MRnumber =     "MR2347016 (2008j:65059)",
  MRreviewer =   "Juan R. Torregrosa",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Aportaciones Mat. Comun.",
  ZMnumber =     "1119.68357",
  acknowledgement = ack-nhfb,
}

@InProceedings{Murata:2006:EKW,
  author =       "T. Murata and K. Saito",
  title =        "Extracting Keywords of {Web} Users' Interests and
                 Visualizing their Routine Visits",
  crossref =     "IEEE:2006:ICC",
  pages =        "1--66",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/ICARCV.2006.345367",
  bibdate =      "Thu May 06 15:04:16 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@InProceedings{Neate:2006:CNF,
  author =       "B. Neate and W. Irwin and N. Churcher",
  title =        "{CodeRank}: a new family of software metrics",
  crossref =     "IEEE:2006:ASE",
  pages =        "369--378 (check??)",
  year =         "2006",
  bibdate =      "Thu May 06 15:14:28 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Ono:2006:IWS,
  author =       "H. Ono and M. Toyoda and M. Kitsuregawa",
  title =        "Identifying {Web} Spam by Densely Connected Sites and
                 its Statistics in a {Japanese Web} Snapshot",
  crossref =     "Barga:2006:IPI",
  pages =        "x131--x131",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/ICDEW.2006.64",
  bibdate =      "Thu May 06 17:00:09 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@Article{Pandurangan:2006:UPC,
  author =       "Gopal Pandurangan and Prabhakar Raghavan and Eli
                 Upfal",
  title =        "Using {PageRank} to characterize web structure",
  journal =      j-INTERNET-MATH,
  volume =       "3",
  number =       "1",
  pages =        "1--20",
  year =         "2006",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (05C07 05C80 68M10); 68M10 68P10 68W05",
  MRnumber =     "MR2283881 (2007k:68135)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1175266365",
  ZMnumber =     "1113.68313",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@InProceedings{Parreira:2006:EDP,
  author =       "Josiane Xavier Parreira and Debora Donato and
                 Sebastian Michel and Gerhard Weikum",
  editor =       "Umeshwar Dayal and others",
  booktitle =    "Proceedings of the 32nd International Conference on
                 Very Large Data Bases",
  title =        "Efficient and decentralized {PageRank} approximation
                 in a peer-to-peer {Web} search network",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "415--426",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/ICDCS.2005.84",
  ISBN =         "1-59593-385-9",
  ISBN-13 =      "978-1-59593-385-0",
  LCCN =         "QA76.9.D3 I61 2006",
  bibdate =      "Sat May 8 18:33:11 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "PageRank-style (PR) link analyses are a cornerstone of
                 Web search engines and Web mining, but they are
                 computationally expensive. Recently, various techniques
                 have been proposed for speeding up these analyses by
                 distributing the link graph among multiple sites.
                 However, none of these advanced methods is suitable for
                 a fully decentralized PR computation in a peer-to-peer
                 (P2P) network with autonomous peers, where each peer
                 can independently crawl Web fragments according to the
                 user's thematic interests. In such a setting the graph
                 fragments that different peers have locally available
                 or know about may arbitrarily overlap among peers,
                 creating additional complexity for the PR
                 computation.This paper presents the JXP algorithm for
                 dynamically and collaboratively computing PR scores of
                 Web pages that are arbitrarily distributed in a P2P
                 network. The algorithm runs at every peer, and it works
                 by combining locally computed PR scores with random
                 meetings among the peers in the network. It is scalable
                 as the number of peers on the network grows, and
                 experiments as well as theoretical arguments show that
                 JXP scores converge to the true PR scores that one
                 would obtain by a centralized computation.",
  acknowledgement = ack-nhfb,
  bookpages =    "xxxi + 1269 (two volumes)",
}

@InProceedings{Peng:2006:RWS,
  author =       "Wen-Chih Peng and Yu-Chin Lin",
  title =        "Ranking {Web} Search Results from Personalized
                 Perspective",
  crossref =     "Wombacher:2006:JCC",
  pages =        "12--12",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/CEC-EEE.2006.72",
  bibdate =      "Thu May 06 15:52:27 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@InProceedings{Quesada:2006:HIP,
  author =       "A. Arratia Quesada and C. Mariju{\'a}n",
  booktitle =    "{Fifth Conference on Discrete Mathematics and Computer
                 Science (Spanish)}",
  title =        "How to improve the {PageRank} of a tree",
  volume =       "23",
  publisher =    "Universidad Valladolid",
  address =      "Secr. Publ. Intercamb. Ed., Valladolid, Spain",
  pages =        "71--78",
  year =         "2006",
  ISBN =         "????",
  ISBN-13 =      "????",
  MRclass =      "05C80 (68U35)",
  MRnumber =     "MR2325945",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Ciencias (Valladolid)",
  ZMnumber =     "05555980",
  acknowledgement = ack-nhfb,
}

@InProceedings{Radev:2006:GBM,
  author =       "D. R. Radev",
  title =        "Graph-Based Methods for Language Processing and
                 Information Retrieval",
  crossref =     "IEEE:2006:ISL",
  pages =        "4--4",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/SLT.2006.326781",
  bibdate =      "Thu May 06 16:42:55 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@InProceedings{Richardson:2006:BPM,
  author =       "Matthew Richardson and Amit Prakash and Eric Brill",
  editor =       "{ACM}",
  booktitle =    "{International World Wide Web Conference Proceedings
                 of the 15th international conference on World Wide
                 Web}",
  title =        "Beyond {PageRank}: machine learning for static
                 ranking",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "707--715",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1145/858476.858479",
  ISBN =         "1-59593-323-9",
  ISBN-13 =      "978-1-59593-323-2",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Since the publication of Brin and Page's paper on
                 PageRank, many in the Web community have depended on
                 PageRank for the static (query-independent) ordering of
                 Web pages. We show that we can significantly outperform
                 PageRank using features that are independent of the
                 link structure of the Web. We gain a further boost in
                 accuracy by using data on the frequency at which users
                 visit Web pages. We use RankNet, a ranking machine
                 learning algorithm, to combine these and other static
                 features based on anchor text and domain
                 characteristics. The resulting model achieves a static
                 ranking pairwise accuracy of 67.3\% (vs. 56.7\% for
                 PageRank or 50\% for random).",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank; RankNet; relevance; search engines; static
                 ranking",
}

@InProceedings{Rungsawang:2006:PAT,
  author =       "Arnon Rungsawang and Bundit Manaskasemsak",
  booktitle =    "{PDP 2006: 14th Euromicro International Conference on
                 Parallel, Distributed, and Network-Based Processing}",
  title =        "Parallel adaptive technique for computing {PageRank}",
  crossref =     "IEEE:2005:EIC",
  pages =        "15--50",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/PDP.2006.55",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1613249",
  abstract =     "Re-ranking the search results using PageRank is a
                 well-known technique used in modern search engines.
                 Running an iterative algorithm like PageRank on a large
                 web graph consumes both much computing resource and
                 time. This paper therefore proposes a parallel adaptive
                 technique for computing PageRank using the PC cluster.
                 Following the study of the Stanford WebBase group on
                 convergence patterns of PageRank scores of pages using
                 the conventional PageRank algorithm, PageRank scores of
                 most pages converge more quickly than the remainder, we
                 then devise our parallel adaptive algorithm to
                 reiterate the computation for pages whose PageRank
                 scores are still not converged. From experiments using
                 a synthesized web graph of 28 million pages and around
                 227 million hyperlinks, we obtain the acceleration rate
                 up to 6-8 times using 32 PC processors.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10741",
  pagecount =    "6",
}

@InProceedings{Sarlos:2006:RRS,
  author =       "Tam{\'a}s Sarl{\'o}s and Adr{\'a}s A. Bencz{\'u}r and
                 K{\'a}roly Csalog{\'a}ny and D{\'a}niel Fogaras and
                 Bal{\'a}zs R{\'a}cz",
  editor =       "ACM",
  booktitle =    "{Proceedings of the 15th international conference on
                 World Wide Web, Edinburgh, Scotland}",
  title =        "To randomize or not to randomize: space optimal
                 summaries for hyperlink analysis",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "297--306",
  year =         "2006",
  DOI =          "http://doi.acm.org/10.1145/1135777.1135823",
  ISBN =         "1-59593-323-9",
  ISBN-13 =      "978-1-59593-323-2",
  LCCN =         "????",
  bibdate =      "Mon May 10 13:56:03 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Personalized PageRank expresses link-based page
                 quality around user selected pages. The only previous
                 personalized PageRank algorithm that can serve on-line
                 queries for an unrestricted choice of pages on large
                 graphs is our Monte Carlo algorithm [WAW 2004]. In this
                 paper we achieve unrestricted personalization by
                 combining rounding and randomized sketching techniques
                 in the dynamic programming algorithm of Jeh and Widom
                 [WWW 2003]. We evaluate the precision of approximation
                 experimentally on large scale real-world data and find
                 significant improvement over previous results. As a key
                 theoretical contribution we show that our algorithms
                 use an optimal amount of space by also improving
                 earlier asymptotic worst-case lower bounds. Our lower
                 bounds and algorithms apply to the SimRank as well; of
                 independent interest is the reduction of the SimRank
                 computation to personalized PageRank.",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@Article{Sun:2006:NPA,
  author =       "Huan Sun and Yimin Wei",
  title =        "A note on the {PageRank} algorithm",
  journal =      j-APPL-MATH-COMP,
  volume =       "179",
  number =       "2",
  pages =        "799--806",
  day =          "15",
  month =        aug,
  year =         "2006",
  CODEN =        "AMHCBQ",
  DOI =          "http://dx.doi.org/10.1016/j.amc.2005.11.120",
  ISSN =         "0096-3003 (print), 1873-5649 (electronic)",
  ISSN-L =       "0096-3003",
  MRclass =      "65F15",
  MRnumber =     "MR2293192",
  bibdate =      "Sat Jul 12 09:02:57 MDT 2008",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.sciencedirect.com/science/journal/00963003",
  URL =          "http://www.math.utah.edu/pub/tex/bib/applmathcomput2005.bib",
  ZMnumber =     "1103.68973",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Mathematics and Computation",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00963003",
}

@InProceedings{Tong:2006:FRW,
  author =       "Hanghang Tong and C. Faloutsos and J.-Y. Pan",
  title =        "Fast Random Walk with Restart and Its Applications",
  crossref =     "Clifton:2006:SIC",
  pages =        "613--622",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/ICDM.2006.70",
  bibdate =      "Thu May 06 16:55:53 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
  xxcrossref =   "Perner:2006:ADM",
}

@Article{Wills:2006:GPM,
  author =       "Rebecca S. Wills",
  title =        "{Google}'s {PageRank}: the math behind the search
                 engine",
  journal =      j-MATH-INTEL,
  volume =       "28",
  number =       "4",
  pages =        "6--11",
  year =         "2006",
  CODEN =        "MAINDC",
  DOI =          "http://dx.doi.org/10.1007/BF02984696",
  ISSN =         "0343-6993 (print), 1866-7414 (electronic)",
  ISSN-L =       "0343-6993",
  MRclass =      "05C80 (00A99 15A18)",
  MRnumber =     "MR2272767",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "The Mathematical Intelligencer",
}

@InProceedings{Wissner-Gross:2006:PTR,
  author =       "A. D. Wissner-Gross",
  title =        "Preparation of Topical Reading Lists from the Link
                 Structure of {Wikipedia}",
  crossref =     "IEEE:2006:SIC",
  pages =        "825--829",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/ICALT.2006.1652568",
  bibdate =      "Thu May 06 16:05:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Yang:2006:PRG,
  author =       "Haixuan Yang and Irwin King and M. R. Lyu",
  title =        "Predictive Random Graph Ranking on the {Web}",
  crossref =     "IEEE:2006:IJC",
  pages =        "1825--1832",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/IJCNN.2006.246901",
  bibdate =      "Thu May 06 16:06:14 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Zhang:2006:XLM,
  author =       "Yi Zhang and Lei Zhang and Yan Zhang and Xiaoming Li",
  title =        "{XRank}: Learning More from {Web} User Behaviors",
  crossref =     "Jeong:2006:SII",
  pages =        "36--36",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/CIT.2006.198",
  bibdate =      "Thu May 06 16:14:21 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Zhuang:2006:ACM,
  author =       "Yueting Zhuang and Hanhuai Shan and Fei Wu",
  booktitle =    "{Proceedings of the 2006 12th International
                 Multi-Media Modelling Conference}",
  title =        "An approach for cross-media retrieval with
                 cross-reference graph and {PageRank}",
  crossref =     "Feng:2006:IMM",
  pages =        "??--??",
  year =         "2006",
  DOI =          "http://dx.doi.org/10.1109/MMMC.2006.1651316",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1651316",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10988",
  pagecount =    "8",
}

@InProceedings{Al-Saffar:2007:EBU,
  author =       "Sinan Al-Saffar and Gregory Heileman",
  editor =       "{IEEE}",
  booktitle =    "{IEEE\slash WIC\slash ACM International Conference on
                 Web Intelligence}",
  title =        "Experimental Bounds on the Usefulness of Personalized
                 and Topic-Sensitive {PageRank}",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "671--675",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/WI.2007.75",
  ISBN =         "0-7695-3026-5",
  ISBN-13 =      "978-0-7695-3026-0",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4427171",
  abstract =     "PageRank is an algorithm used by several search
                 engines to rank web documents according to their
                 assumed relevance and popularity deduced from the Web's
                 link structure. PageRank determines a global ordering
                 of candidate search results according to each page's
                 popularity as determined by the number and importance
                 of pages linking to these results. Personalized and
                 topic-sensitive PageRank are variants of the algorithm
                 that return a local ranking based on each user's
                 preferences as biased by a set of pages they trust or
                 topics they prefer. In this paper we compare
                 personalized and topic-sensitive local PageRanks to the
                 global PageRank showing experimentally how similar or
                 dissimilar results of personalization can be to the
                 original global rank results and to other
                 personalizations. Our approach is to examine a snapshot
                 of the Web and determine how advantageous
                 personalization can be in the best and worst cases and
                 how it performs at various values of the damping factor
                 in the PageRank formula.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4427043",
}

@InProceedings{Al-Saffar:2007:PTS,
  author =       "S. Al-Saffar and G. Heileman",
  title =        "Personalized and Topic-Sensitive {PageRank}",
  crossref =     "Lin:2007:PIW",
  pages =        "671--675",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/WI.2007.75",
  bibdate =      "Fri Feb 19 15:48:36 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Andersen:2007:DSD,
  author =       "Reid Andersen and Fan Chung",
  title =        "Detecting sharp drops in {PageRank} and a simplified
                 local partitioning algorithm",
  crossref =     "Cai:2007:TAM",
  pages =        "1--12",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-72504-6_1",
  MRclass =      "68M10 (68U35)",
  MRnumber =     "MR2374293 (2008m:68006)",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "05211353",
  abstract =     "We show that whenever there is a sharp drop in the
                 numerical rank defined by a personalized PageRank
                 vector, the location of the drop reveals a cut with
                 small conductance. We then show that for any cut in the
                 graph, and for many starting vertices within that cut,
                 an approximate personalized PageRank vector will have a
                 sharp drop sufficient to produce a cut with conductance
                 nearly as small as the original cut. Using this
                 technique, we produce a nearly linear time local
                 partitioning algorithm whose analysis is simpler than
                 previous algorithms.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Andersen:2007:LCP,
  author =       "Reid Andersen and Christian Borgs and Jennifer Chayes
                 and John Hopcraft and Vahab S. Mirrokni and Shang-Hua
                 Teng",
  title =        "Local computation of {PageRank} contributions",
  crossref =     "Bonato:2007:AMW",
  pages =        "150--165",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-77004-6_12",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "05C90 (68R10 68U35 68W25)",
  MRnumber =     "MR2504913 (2010f:05175)",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  acknowledgement = ack-nhfb,
}

@InProceedings{Andersen:2007:LPD,
  author =       "Reid Andersen and Fan Chung and Kevin Lang",
  title =        "Local partitioning for directed graphs using
                 {PageRank}",
  crossref =     "Bonato:2007:AMW",
  pages =        "166--178",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-77004-6_13",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "05C20 (68M10 68R10 68U35)",
  MRnumber =     "MR2504914 (2010f:05082)",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  acknowledgement = ack-nhfb,
}

@Article{Andersen:2007:UPL,
  author =       "Reid Andersen and Fan Chung and Kevin Lang",
  title =        "Using {PageRank} to locally partition a graph",
  journal =      j-INTERNET-MATH,
  volume =       "4",
  number =       "1",
  pages =        "35--64",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "05C70 (05C50 05C85 05C90 68R10)",
  MRnumber =     "MR2492174 (2009k:05142)",
  MRreviewer =   "Anthony Bonato",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1243430567",
  ZMnumber =     "1170.68302",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@InProceedings{Avrachenkov:2007:DPM,
  author =       "Konstantin Avrachenkov and Nelly Litvak and Kim Son
                 Pham",
  title =        "Distribution of {PageRank} mass among principle
                 components of the web",
  crossref =     "Bonato:2007:AMW",
  pages =        "16--28",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-77004-6_2",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "68U35 (15A18)",
  MRnumber =     "MR2504904",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  ZMnumber =     "1136.68319",
  acknowledgement = ack-nhfb,
}

@Article{Avrachenkov:2007:MCM,
  author =       "K. Avrachenkov and N. Litvak and D. Nemirovsky and N.
                 Osipova",
  title =        "{Monte Carlo} Methods in {PageRank} Computation: When
                 One Iteration is Sufficient",
  journal =      j-SIAM-J-NUMER-ANAL,
  volume =       "45",
  number =       "2",
  pages =        "890--904",
  month =        feb,
  year =         "2007",
  CODEN =        "SJNAAM",
  DOI =          "http://dx.doi.org/10.1137/050643799",
  ISSN =         "0036-1429 (print), 1095-7170 (electronic)",
  ISSN-L =       "0036-1429",
  MRclass =      "60J20 (60J10 65C05); 60J20 65C05 60J05 60J10 65C40",
  MRnumber =     "MR2300301",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://siamdl.aip.org/dbt/dbt.jsp?KEY=SJNAAM&Volume=45&Issue=2;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/siamjnumeranal2000.bib",
  ZMnumber =     "1146.60056",
  abstract =     "PageRank is one of the principle criteria according to
                 which Google ranks Web pages. PageRank can be
                 interpreted as a frequency of visiting a Web page by a
                 random surfer, and thus it reflects the popularity of a
                 Web page. Google computes the PageRank using the power
                 iteration method, which requires about one week of
                 intensive computations. In the present work we propose
                 and analyze Monte Carlo-type methods for the PageRank
                 computation. There are several advantages of the
                 probabilistic Monte Carlo methods over the
                 deterministic power iteration method: Monte Carlo
                 methods already provide good estimation of the PageRank
                 for relatively important pages after one iteration;
                 Monte Carlo methods have natural parallel
                 implementation; and finally, Monte Carlo methods allow
                 one to perform continuous update of the PageRank as the
                 structure of the Web changes.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Numerical Analysis",
  journal-URL =  "http://epubs.siam.org/sinum",
  keywords =     "absorbing Markov chains; Google; Monte Carlo methods;
                 PageRank",
}

@InProceedings{Bickson:2007:PPR,
  author =       "D. Bickson and D. Malkhi and Lidong Zhou",
  title =        "Peer-to-Peer Rating",
  crossref =     "Hauswirth:2007:SII",
  pages =        "211--218",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/P2P.2007.36",
  bibdate =      "Thu May 06 16:58:10 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@InProceedings{Bidoki:2007:FFI,
  author =       "A. M. Z. Bidoki and N. Yazdani and P. Ghodsnia",
  title =        "{FICA}: {A} Fast Intelligent Crawling Algorithm",
  crossref =     "Lin:2007:PIW",
  pages =        "635--641",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/WI.2007.91",
  bibdate =      "Thu May 06 16:57:29 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@InBook{Boldi:2007:DIP,
  author =       "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
  title =        "A deeper investigation of {PageRank} as a function of
                 the damping factor",
  volume =       "07071",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "????",
  year =         "2007",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Feb 19 15:32:30 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2007/1072/pdf/07071.VignaSebastiano.Paper.1072",
  acknowledgement = ack-nhfb,
}

@InCollection{Brezinski:2007:EMP,
  author =       "Claude Brezinski and Michela Redivo-Zaglia",
  editor =       "Andreas Frommer and Michael W. Mahoney and Daniel B.
                 Szyld",
  booktitle =    "{Web} Information Retrieval and Linear Algebra
                 Algorithms",
  title =        "Extrapolation and minimization procedures for the
                 {PageRank} vector",
  volume =       "07071",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "1862--????",
  year =         "2007",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Feb 19 15:32:30 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2007/1068/pdf/07071.RedivoZagliaMichela.Paper.1068",
  acknowledgement = ack-nhfb,
}

@InProceedings{Caverlee:2007:SRW,
  author =       "J. Caverlee and S. Webb and L. Liu",
  title =        "Spam-Resilient {Web} Rankings via Influence
                 Throttling",
  crossref =     "IEEE:2007:ICI",
  pages =        "1--10",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/IPDPS.2007.370233",
  bibdate =      "Thu May 06 15:12:50 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Chakrabarti:2007:DPP,
  author =       "Soumen Chakrabarti",
  editor =       "{ACM}",
  booktitle =    "Proceedings of the 16th international conference on
                 World Wide Web",
  title =        "Dynamic personalized {PageRank} in entity-relation
                 graphs",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "571--580",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1016/S0306-4573(96)85003-5",
  ISBN =         "1-59593-654-8",
  ISBN-13 =      "978-1-59593-654-7",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:07 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Extractors and taggers turn unstructured text into
                 entity-relation(ER) graphs where nodes are entities
                 (email, paper, person,conference, company) and edges
                 are relations (wrote, cited,works-for). Typed proximity
                 search of the form {\bf type=person NEAR company~'IBM',
                 paper~'XML'} is an increasingly useful search paradigm
                 in ER graphs. Proximity search implementations either
                 perform a Pagerank-like computation at query time,
                 which is slow, or precompute, store and combine
                 per-word Pageranks, which can be very expensive in
                 terms of preprocessing time and space. We present
                 HubRank, a new system for fast, dynamic,
                 space-efficient proximity searches in ER graphs. During
                 preprocessing, HubRank computes and indexes certain
                 'sketchy' random walk fingerprints for a small fraction
                 of nodes, carefully chosen using query log statistics.
                 At query time, a small 'active' subgraph is identified,
                 bordered by nodes with indexed fingerprints. These
                 fingerprints are adaptively loaded to various
                 resolutions to form approximate personalized Pagerank
                 vectors (PPVs). PPVs at remaining active nodes are now
                 computed iteratively. We report on experiments with
                 CiteSeer's ER graph and millions of real Cite Seer
                 queries. Some representative numbers follow. On our
                 testbed, HubRank preprocesses and indexes 52 times
                 faster than whole-vocabulary PPV computation. A text
                 index occupies 56 MB. Whole-vocabulary PPVs would
                 consume 102GB. If PPVs are truncated to 56 MB,
                 precision compared to true Pagerank drops to 0.55; in
                 contrast, HubRank has precision 0.91 at 63MB. HubRank's
                 average query time is 200-300 milliseconds; query-time
                 Pagerank computation takes 11 seconds on average.",
  acknowledgement = ack-nhfb,
  keywords =     "graph proximity search; personalized pagerank",
}

@Article{Chau:2007:IWA,
  author =       "M. Chau and H. Chen",
  title =        "Incorporating {Web} Analysis Into Neural Networks: An
                 Example in {Hopfield} Net Searching",
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics,
                 Part C: Applications and Reviews",
  volume =       "37",
  number =       "3",
  pages =        "352--358",
  month =        may,
  year =         "2007",
  CODEN =        "????",
  DOI =          "http://dx.doi.org/10.1109/TSMCC.2007.893277",
  ISSN =         "1094-6977",
  bibdate =      "Thu May 06 16:34:52 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@Article{Chung:2007:HKP,
  author =       "Fan Chung",
  title =        "The heat kernel as the pagerank of a graph",
  journal =      j-PROC-NATL-ACAD-SCI-USA,
  volume =       "104",
  number =       "50",
  pages =        "19735--19740",
  day =          "5",
  month =        dec,
  year =         "2007",
  CODEN =        "PNASA6",
  DOI =          "http://dx.doi.org/10.1073/pnas.0708838104.",
  ISSN =         "0027-8424 (print), 1091-6490 (electronic)",
  ISSN-L =       "0027-8424",
  bibdate =      "Fri Jun 3 10:03:23 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2148367",
  abstract =     "The concept of pagerank was first started as a way for
                 determining the ranking of Web pages by Web search
                 engines. Based on relations in interconnected networks,
                 pagerank has become a major tool for addressing
                 fundamental problems arising in general graphs,
                 especially for large information networks with hundreds
                 of thousands of nodes. A notable notion of pagerank,
                 introduced by Brin and Page and denoted by PageRank, is
                 based on random walks as a geometric sum. In this
                 paper, we consider a notion of pagerank that is based
                 on the (discrete) heat kernel and can be expressed as
                 an exponential sum of random walks. The heat kernel
                 satisfies the heat equation and can be used to analyze
                 many useful properties of random walks in a graph. A
                 local Cheeger inequality is established, which implies
                 that, by focusing on cuts determined by linear
                 orderings of vertices using the heat kernel pageranks,
                 the resulting partition is within a quadratic factor of
                 the optimum. This is true, even if we restrict the
                 volume of the small part separated by the cut to be
                 close to some specified target value. This leads to a
                 graph partitioning algorithm for which the running time
                 is proportional to the size of the targeted volume
                 (instead of the size of the whole graph).",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the National Academy of Sciences of the United States of America",
  journal-URL =  "http://www.pnas.org/search",
}

@InProceedings{Constantine:2007:UPC,
  author =       "Paul G. Constantine and David F. Gleich",
  title =        "Using polynomial chaos to compute the influence of
                 multiple random surfers in the {PageRank} model",
  crossref =     "Bonato:2007:AMW",
  pages =        "82--95",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-77004-6_7",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "68U35 (60G99 65C05 68W40)",
  MRnumber =     "MR2505172",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  ZMnumber =     "1136.68321",
  acknowledgement = ack-nhfb,
}

@InProceedings{Costache:2007:PPB,
  author =       "Stefania Costache and Wolfgang Nejdl and Raluca Paiu",
  editor =       "Anonymous",
  booktitle =    "Proceedings of the 19th International Conference on
                 Advanced Information Systems Engineering",
  title =        "Personalizing {PageRank-based} ranking over
                 distributed collections",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "111--126",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1145/511446.511513",
  ISBN =         "0-7918-4804-3",
  ISBN-13 =      "978-0-7918-4804-3",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "TA174 .D4623 2007",
  bibdate =      "Sat May 8 18:33:11 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  abstract =     "In distributed work environments, where users are
                 sharing and searching resources, ensuring an
                 appropriate ranking at remote peers is a key problem.
                 While this issue has been investigated for federated
                 libraries, where the exchange of collection specific
                 information suffices to enable homogeneous TFxIDF
                 rankings across the participating collections, no
                 solutions are known for PageRank-based ranking schemes,
                 important for personalized retrieval on the
                 desktop.\par

                 Connected users share fulltext resources and metadata
                 expressing information about them and connecting them.
                 Based on which information is shared or private, we
                 propose several algorithms for computing personalized
                 PageRank-based rankings for these connected peers. We
                 discuss which information is needed for the ranking
                 computation and how Page-Rank values can be estimated
                 in case of incomplete information. We analyze the
                 performance of our algorithms through a set of
                 experiments, and conclude with suggestions for choosing
                 among these algorithms.",
  acknowledgement = ack-nhfb,
  keywords =     "distributed search; pagerank; personalization;
                 privacy",
}

@Article{DelCorso:2007:CKS,
  author =       "Gianna M. {Del Corso} and Antonio Gull{\'\i} and
                 Francesco Romani",
  title =        "Comparison of {Krylov} subspace methods on the
                 {PageRank} problem",
  journal =      j-J-COMP-APPL-MATH,
  volume =       "210",
  number =       "1--2",
  pages =        "159--166",
  year =         "2007",
  CODEN =        "JCAMDI",
  DOI =          "http://dx.doi.org/10.1016/j.cam.2006.10.080",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  MRclass =      "65F15 (65Y20)",
  MRnumber =     "MR2389165 (2009b:65096)",
  MRreviewer =   "Valeria Ruggiero",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1134.65026",
  abstract =     "PageRank algorithm plays a very important role in
                 search engine technology and consists in the
                 computation of the eigenvector corresponding to the
                 eigenvalue one of a matrix whose size is now in the
                 billions. The problem incorporates a parameter @a that
                 determines the difficulty of the problem. In this
                 paper, the effectiveness of stationary and
                 nonstationary methods are compared on some portion of
                 real web matrices for different choices of @a. We see
                 that stationary methods are very reliable and more
                 competitive when the problem is well conditioned, that
                 is for small values of @a. However, for large values of
                 the parameter @a the problem becomes more difficult and
                 methods such as preconditioned BiCGStab or restarted
                 preconditioned GMRES become competitive with stationary
                 methods in terms of Mflops count as well as in number
                 of iterations necessary to reach convergence.",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
}

@Article{Djerassi:2007:BRW,
  author =       "Carl Djerassi",
  title =        "Book Reviews: When acting speaks louder than words:
                 Science on Stage: {{\booktitle{From `Doctor Faustus' to
                 `Copenhagen'}}, by Kirsten Shepherd-Barr.
                 \booktitle{Google's PageRank and Beyond: The Science of
                 Search Engine Rankings}, by Amy N. Langville and Carl
                 D. Meyer. \booktitle{Broken Genius The Rise and Fall of
                 William Shockley, Creator of the Electronic Age}, by
                 Joel N. Shurkin}",
  journal =      j-PHYS-TODAY,
  volume =       "60",
  number =       "2",
  pages =        "63--64",
  year =         "2007",
  CODEN =        "PHTOAD",
  DOI =          "http://dx.doi.org/10.1063/1.2711638",
  ISSN =         "0031-9228 (print), 1945-0699 (electronic)",
  ISSN-L =       "0031-9228",
  bibdate =      "Wed Sep 12 15:15:45 MDT 2012",
  bibsource =    "http://www.math.utah.edu/pub/bibnet/authors/b/bohr-niels.bib;
                 http://www.math.utah.edu/pub/bibnet/authors/h/heisenberg-werner.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.aip.org/link/phtoad/v60/i2/p63/s1",
  acknowledgement = ack-nhfb,
  fjournal =     "Physics Today",
  journal-URL =  "http://www.physicstoday.org/",
  keywords =     "Copenhagen; Michael Frayn; Niels Bohr; Werner
                 Heisenberg",
}

@Article{Donato:2007:WGH,
  author =       "Debora Donato and Luigi Laura and Stefano Leonardi and
                 Stefano Millozzi",
  title =        "The {Web} as a graph: {How} far we are",
  journal =      j-TOIT,
  volume =       "7",
  number =       "1",
  pages =        "4:1--4:??",
  month =        feb,
  year =         "2007",
  CODEN =        "????",
  DOI =          "http://doi.acm.org/10.1145/1189740.1189744",
  ISSN =         "1533-5399 (print), 1557-6051 (electronic)",
  ISSN-L =       "1533-5399",
  bibdate =      "Mon Jun 16 10:57:52 MDT 2008",
  bibsource =    "http://www.acm.org/pubs/contents/journals/toit/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/toit.bib",
  abstract =     "In this article we present an experimental study of
                 the properties of webgraphs. We study a large crawl
                 from 2001 of 200M pages and about 1.4 billion edges,
                 made available by the WebBase project at Stanford, as
                 well as several synthetic ones generated according to
                 various models proposed recently. We investigate
                 several topological properties of such graphs,
                 including the number of bipartite cores and strongly
                 connected components, the distribution of degrees and
                 PageRank values and some correlations; we present a
                 comparison study of the models against these
                 measures.Our findings are that (i) the WebBase sample
                 differs slightly from the (older) samples studied in
                 the literature, and (ii) despite the fact that these
                 models do not catch all of its properties, they do
                 exhibit some peculiar behaviors not found, for example,
                 in the models from classical random graph
                 theory.Moreover we developed a software library able to
                 generate and measure massive graphs in secondary
                 memory; this library is publicy available under the GPL
                 licence. We discuss its implementation and some
                 computational issues related to secondary memory graph
                 algorithms.",
  acknowledgement = ack-nhfb,
  articleno =    "4",
  fjournal =     "ACM Transactions on Internet Technology (TOIT)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J780",
  keywords =     "graph structure; models; World-Wide-Web",
}

@Article{Douglis:2007:ECW,
  author =       "Fred Douglis",
  title =        "From the {Editor in Chief}: What's Your {PageRank}?",
  journal =      j-IEEE-INTERNET-COMPUT,
  volume =       "11",
  number =       "4",
  pages =        "3--4",
  month =        jul # "\slash " # aug,
  year =         "2007",
  CODEN =        "IICOFX",
  DOI =          "http://dx.doi.org/10.1109/MIC.2007.82",
  ISSN =         "1089-7801",
  ISSN-L =       "1089-7801",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4270541",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4236",
  fjournal =     "IEEE Internet Computing",
}

@InProceedings{Du:2007:USF,
  author =       "Ye Du and Yaoyun Shi and Xin Zhao",
  editor =       "{ACM}",
  booktitle =    "AIRWeb; Vol. 215 Proceedings of the 3rd international
                 workshop on Adversarial information retrieval on the
                 web",
  title =        "Using spam farm to boost {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "29--36",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1145/1062745.1062762",
  ISBN =         "1-59593-732-3",
  ISBN-13 =      "978-1-59593-732-2",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Nowadays web spamming has emerged to take the economic
                 advantage of high search rankings and threatened the
                 accuracy and fairness of those rankings. Understanding
                 spamming techniques is essential for evaluating the
                 strength and weakness of a ranking algorithm, and for
                 fighting against web spamming. In this paper, we
                 identify the optimal spam farm structure under some
                 realistic assumptions in the single target spam farm
                 model. Our result extends the optimal spam farm claimed
                 by Gy{\"o}ngyi and Garcia-Molina through dropping the
                 assumption that leakage is constant. We also
                 characterize the optimal spam farms under additional
                 constraints, which the spammer may deploy to disguise
                 the spam farm by deviating from the unconstrained
                 optimal structure.",
  acknowledgement = ack-nhfb,
  keywords =     "link spamming; Markov chain; PageRank algorithm",
}

@Article{Eirinaki:2007:WSP,
  author =       "Magdalini Eirinaki and Michalis Vazirgiannis",
  title =        "{Web} site personalization based on link analysis and
                 navigational patterns",
  journal =      j-TOIT,
  volume =       "7",
  number =       "4",
  pages =        "21:1--21:??",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "http://doi.acm.org/10.1145/1278366.1278370",
  ISSN =         "1533-5399 (print), 1557-6051 (electronic)",
  ISSN-L =       "1533-5399",
  bibdate =      "Mon Jun 16 10:58:47 MDT 2008",
  bibsource =    "http://www.acm.org/pubs/contents/journals/toit/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/toit.bib",
  abstract =     "The continuous growth in the size and use of the World
                 Wide Web imposes new methods of design and development
                 of online information services. The need for predicting
                 the users' needs in order to improve the usability and
                 user retention of a Web site is more than evident and
                 can be addressed by personalizing it. Recommendation
                 algorithms aim at proposing ``next'' pages to users
                 based on their current visit and past users'
                 navigational patterns. In the vast majority of related
                 algorithms, however, only the usage data is used to
                 produce recommendations, disregarding the structural
                 properties of the Web graph. Thus important---in terms
                 of PageRank authority score---pages may be underrated.
                 In this work, we present UPR, a PageRank-style
                 algorithm which combines usage data and link analysis
                 techniques for assigning probabilities to Web pages
                 based on their importance in the Web site's
                 navigational graph. We propose the application of a
                 localized version of UPR ( l-UPR ) to personalized
                 navigational subgraphs for online Web page ranking and
                 recommendation. Moreover, we propose a hybrid
                 probabilistic predictive model based on Markov models
                 and link analysis for assigning prior probabilities in
                 a hybrid probabilistic model. We prove, through
                 experimentation, that this approach results in more
                 objective and representative predictions than the ones
                 produced from the pure usage-based approaches.",
  acknowledgement = ack-nhfb,
  articleno =    "21",
  fjournal =     "ACM Transactions on Internet Technology (TOIT)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J780",
  keywords =     "link analysis; Markov models; recommendations;
                 usage-based PageRank; Web personalization",
}

@Article{Fortunato:2007:LEP,
  author =       "Santo Fortunato and Mari{\'a}n Bogu{\~n}{\'a} and
                 Alessandro Flammini and Filippo Menczer",
  title =        "On local estimations of {PageRank}: a mean field
                 approach",
  journal =      j-INTERNET-MATH,
  volume =       "4",
  number =       "2-3",
  pages =        "245--266",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "60G50 (60J20 68M10)",
  MRnumber =     "MR2522878",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1243430608",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@Article{Fortunato:2007:RWD,
  author =       "Santo Fortunato and Alessandro Flammini",
  title =        "Random walks on directed networks: the case of
                 {PageRank}",
  journal =      j-INT-J-BIFURC-CHAOS-APPL-SCI-ENG,
  volume =       "17",
  number =       "7",
  pages =        "2343--2353",
  year =         "2007",
  CODEN =        "IJBEE4",
  DOI =          "http://dx.doi.org/10.1142/S0218127407018439",
  ISSN =         "0218-1274",
  MRclass =      "60G50 (05C38 68M10); 60G50 05C38 68M10 82B41",
  MRnumber =     "MR2349743 (2008h:60171)",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1142.68311",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Bifurcation and Chaos in
                 Applied Sciences and Engineering",
}

@InBook{Gleich:2007:TRP,
  author =       "David Gleich and Peter Glynn and Gene Golub and Chen
                 Greif",
  title =        "Three results on the {PageRank} vector:
                 eigenstructure, sensitivity, and the derivative",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "????",
  year =         "2007",
  ISBN =         "????",
  ISBN-13 =      "????",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 10:03:23 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings 07071",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2007/1061/pdf/07071.GleichDavid.Paper.1061",
  acknowledgement = ack-nhfb,
}

@InBook{Gray:2007:IOS,
  author =       "Andrew P. Gray and Chen Greif and Tracy Lau",
  title =        "An inner, outer stationary iteration for computing
                 {PageRank}",
  volume =       "07071",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "????",
  year =         "2007",
  ISBN =         "????",
  ISBN-13 =      "????",
  LCCN =         "????",
  bibdate =      "Fri Feb 19 15:32:30 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2007/1062/pdf/07071.GreifChen.Paper.1062",
  acknowledgement = ack-nhfb,
}

@InProceedings{Guo:2007:MAC,
  author =       "Ye Guo",
  title =        "{MixPR} --- An Approach of Combining Content and Links
                 of {Web} Page[s]",
  crossref =     "Lei:2007:FPF",
  volume =       "2",
  pages =        "456--460",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/FSKD.2007.407",
  bibdate =      "Thu May 06 15:23:46 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Guo:2007:PPW,
  author =       "Yong Zhen Guo and Kotagiri Ramamohanarao and Laurence
                 A. F. Park",
  booktitle =    "{IEEE\slash WIC\slash ACM International Conference on
                 Web Intelligence}",
  title =        "Personalized {PageRank} for {Web} Page Prediction
                 Based on Access Time-Length and Frequency",
  crossref =     "Lin:2007:PIW",
  pages =        "687--690",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/WI.2007.58",
  ISBN =         "0-7695-3026-5",
  ISBN-13 =      "978-0-7695-3026-0",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4427174",
  abstract =     "Web page prefetching techniques are used to address
                 the access latency problem of the Internet. To perform
                 successful prefetching, we must be able to predict the
                 next set of pages that will be accessed by users. The
                 PageRank algorithm used by Google is able to compute
                 the popularity of a set of Web pages based on their
                 link structure. In this paper, a novel PageRank-like
                 algorithm is proposed for conducting Web page
                 prediction. Two biasing factors are adopted to
                 personalize PageRank, so that it favors the pages that
                 are more important to users. One factor is the length
                 of time spent on visiting a page and the other is the
                 frequency that a page was visited. The experiments
                 conducted show that using these two factors
                 simultaneously to bias PageRank results in more
                 accurate Web page prediction.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4427043",
}

@Article{He:2007:CSW,
  author =       "Xiaofei He and Deng Cai and Ji-Rong Wen and Wei-Ying
                 Ma and Hong-Jiang Zhang",
  title =        "Clustering and searching {WWW} images using link and
                 page layout analysis",
  journal =      j-TOMCCAP,
  volume =       "3",
  number =       "2",
  pages =        "10:1--10:??",
  month =        may,
  year =         "2007",
  CODEN =        "????",
  DOI =          "http://doi.acm.org/10.1145/1230812.1230816",
  ISSN =         "1551-6857 (print), 1551-6865 (electronic)",
  ISSN-L =       "1551-6857",
  bibdate =      "Mon Jun 16 17:10:04 MDT 2008",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tomccap/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/tomccap.bib",
  abstract =     "Due to the rapid growth of the number of digital
                 images on the Web, there is an increasing demand for an
                 effective and efficient method for organizing and
                 retrieving the available images. This article describes
                 iFind, a system for clustering and searching WWW
                 images. By using a vision-based page segmentation
                 algorithm, a Web page is partitioned into blocks, and
                 the textual and link information of an image can be
                 accurately extracted from the block containing that
                 image. The textual information is used for image
                 indexing. By extracting the page-to-block,
                 block-to-image, block-to-page relationships through
                 link structure and page layout analysis, we construct
                 an image graph. Our method is less sensitive to noisy
                 links than previous methods like PageRank, HITS, and
                 PicASHOW, and hence the image graph can better reflect
                 the semantic relationship between images. Using the
                 notion of Markov Chain, we can compute the limiting
                 probability distributions of the images, ImageRanks,
                 which characterize the importance of the images. The
                 ImageRanks are combined with the relevance scores to
                 produce the final ranking for image search. With the
                 graph models, we can also use techniques from spectral
                 graph theory for image clustering and embedding, or 2-D
                 visualization. Some experimental results on 11.6
                 million images downloaded from the Web are provided in
                 the article.",
  acknowledgement = ack-nhfb,
  articleno =    "10",
  fjournal =     "ACM Transactions on Multimedia Computing, Communications, and Applications",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J961",
  keywords =     "image clustering; image search; link analysis; Web
                 mining",
}

@InProceedings{Hussain:2007:SARa,
  author =       "F. K. Hussain and E. Chang and O. K. Hussain",
  title =        "State of the art review of the existing {PageRank}
                 based algorithms for trust computation",
  crossref =     "Dini:2007:SIC",
  pages =        "75--75",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/ICSNC.2007.78",
  bibdate =      "Thu May 06 15:25:50 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In this paper we present a state of the art review of
                 PageRank based approaches for trust and reputation
                 computation. We divide the approaches that make use of
                 PageRank method for trust and reputation computation,
                 into six different classes. Each of the six classes is
                 discussed in this paper.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Hussain:2007:SARb,
  author =       "F. K. Hussain and E. Chang and O. K. Hussain",
  title =        "State of the art review of the existing {PageRank}
                 based algorithms for trust and reputation computation",
  crossref =     "Ramakrishnan:2007:PSI",
  pages =        "43--43",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/ICIMP.2007.44",
  bibdate =      "Thu May 06 16:11:46 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@Article{Ipsen:2007:PCS,
  author =       "Ilse C. F. Ipsen and Teresa M. Selee",
  title =        "{PageRank} computation, with special attention to
                 dangling nodes",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "29",
  number =       "4",
  pages =        "1281--1296",
  month =        nov,
  year =         "2007",
  CODEN =        "SJMAEL",
  DOI =          "http://dx.doi.org/10.1137/060664331",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  MRclass =      "65C40 (15A06 15A18 68M10 68P20)",
  MRnumber =     "MR2369296 (2009a:65013)",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1156.65038",
  abstract =     "We present a simple algorithm for computing the
                 PageRank (stationary distribution) of the stochastic
                 Google matrix $G$. The algorithm lumps all dangling
                 nodes into a single node. We express lumping as a
                 similarity transformation of $G$ and show that the
                 PageRank of the nondangling nodes can be computed
                 separately from that of the dangling nodes. The
                 algorithm applies the power method only to the smaller
                 lumped matrix, but the convergence rate is the same as
                 that of the power method applied to the full matrix
                 $G$. The efficiency of the algorithm increases as the
                 number of dangling nodes increases. We also extend the
                 expression for PageRank and the algorithm to more
                 general Google matrices that have several different
                 dangling node vectors, when it is required to
                 distinguish among different classes of dangling nodes.
                 We also analyze the effect of the dangling node vector
                 on the PageRank and show that the PageRank of the
                 dangling nodes depends strongly on that of the
                 nondangling nodes but not vice versa. Last we present a
                 Jordan decomposition of the Google matrix for the
                 (theoretical) extreme case when all Web pages are
                 dangling nodes.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
  keywords =     "Google; Jordan decomposition; lumping; power method;
                 rank-one matrix; similarity transformation; stationary
                 distribution; stochastic matrix",
}

@InProceedings{Jiang:2007:SBC,
  author =       "Qiancheng Jiang and Yan Zhang",
  title =        "{SiteRank}-Based Crawling Ordering Strategy for Search
                 Engines",
  crossref =     "Miyazaki:2007:CPI",
  pages =        "259--263",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/CIT.2007.35",
  bibdate =      "Thu May 06 15:48:26 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Kao:2007:FPC,
  author =       "Hung-Yu Kao and Seng-Feng Lin",
  booktitle =    "{IEEE\slash WIC\slash ACM International Conference on
                 Web Intelligence}",
  title =        "A Fast {PageRank} Convergence Method based on the
                 Cluster Prediction",
  crossref =     "Lin:2007:PIW",
  pages =        "593--599",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/WI.2007.129",
  ISBN =         "0-7695-3026-5",
  ISBN-13 =      "978-0-7695-3026-0",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4427158",
  abstract =     "In recent years, search engines have already played
                 the key roles among Web applications, and link analysis
                 algorithms are the major methods to measure the
                 important values of Web pages. These algorithms employ
                 the conventional flat Web graph built by Web pages and
                 link relations of Web pages to obtain the relative
                 importance of Web objects. Previous researches have
                 observed that PageRank-like link analysis algorithms
                 have a bias against newly created Web pages. A new
                 ranking algorithm called Page Quality was then proposed
                 to solve this issue. Page Quality predicates future
                 ranking values by the difference rate between the
                 current ranking value and the previous ranking value.
                 In this paper, we propose a new algorithm called DRank
                 to diminish the bias of PageRank-like link analysis
                 algorithms, and attain the better performance than Page
                 Quality. In this algorithm, we model Web graph as a
                 three-layer graph which includes Host Graph, Directory
                 Graph and Page Graph by using the hierarchical
                 structure of URLs and the structure of link relation of
                 Web pages. We calculate the importance of Hosts,
                 Directories and Pages by weighted graph we built and
                 then the clustering distribution of PageRank values of
                 pages within directories is observed. We can then
                 predicate the more accurate values of page importance
                 to diminish the bias of newly created pages by the
                 clustering characteristic of PageRank. Experiment
                 results show that DRank algorithm works well on
                 predicating future ranking values of pages and
                 outperform Page Quality.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4427043",
}

@InProceedings{Kohlschutter:2007:UAT,
  author =       "Christian Kohlsch{\"u}tter and Paul-Alexandru Chirita
                 and Wolfgang Nejdl",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceedings of
                 the 16th international conference on World Wide Web",
  title =        "Utility analysis for topically biased {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "1211--1212",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1145/511446.511513",
  ISBN =         "1-59593-654-8",
  ISBN-13 =      "978-1-59593-654-7",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "PageRank is known to be an efficient metric for
                 computing general document importance in the Web. While
                 commonly used as a one-size-fits-all measure, the
                 ability to produce topically biased ranks has not yet
                 been fully explored in detail. In particular, it was
                 still unclear to what granularity of 'topic' the
                 computation of biased page ranks makes sense. In this
                 paper we present the results of a thorough quantitative
                 and qualitative analysis of biasing PageRank on Open
                 Directory categories. We show that the MAP quality of
                 Biased PageRank generally increases with the ODP level
                 up to a certain point, thus sustaining the usage of
                 more specialized categories to bias PageRank on, in
                 order to improve topic specific search.",
  acknowledgement = ack-nhfb,
  keywords =     "biased PageRank; open directory; personalized search",
}

@InBook{Kollias:2007:APC,
  author =       "Giorgos Kollias and Efstratios Gallopoulos",
  title =        "Asynchronous {PageRank} computation in an interactive
                 multithreading environment",
  volume =       "07071",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "????",
  year =         "2007",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Feb 19 15:32:30 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2007/1065/pdf/07071.KolliasGiorgios.Paper.1065",
  acknowledgement = ack-nhfb,
}

@Article{Lee:2007:TSA,
  author =       "Chris P. Lee and Gene H. Golub and Stefanos A.
                 Zenios",
  title =        "A two-stage algorithm for computing {PageRank} and
                 multistage generalizations",
  journal =      j-INTERNET-MATH,
  volume =       "4",
  number =       "4",
  pages =        "299--327",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68M11",
  MRnumber =     "MR2522947",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1243430809",
  abstract =     "The PageRank model pioneered by Google is the most
                 common approach for generating web search results. We
                 present a two-stage algorithm for computing the
                 PageRank vector where the algorithm exploits the
                 lumpability of the underlying Markov chain. We make
                 three contributions. First, the algorithm speeds up the
                 PageRank calculation significantly. With web graphs
                 having millions of webpages, the speed-up is typically
                 in the two- to three-fold range. The algorithm can also
                 embed other acceleration methods such as quadratic
                 extrapolation, the Gauss-Seidel method, or the
                 Biconjugate gradient stable method for an even greater
                 speed-up; cumulative speed-up is as high as 7 to 14
                 times. The second contribution relates to the handling
                 of dangling nodes. Conventionally, dangling nodes are
                 included only towards the end of the computation. While
                 this approach works reasonably well, it can fail in
                 extreme cases involving aggressive personalization. We
                 prove that our algorithm is the generally correct way
                 of handling dangling nodes using probabilistic
                 arguments. We also discuss variants of our algorithm,
                 including a multistage extension for calculating a
                 generalized version of the PageRank model where
                 different personalization vectors are used for webpages
                 of different classes. The ability to form class
                 associations may be useful for building more refined
                 models of web traffic.",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@InProceedings{Li:2007:HCN,
  author =       "Cun-he Li and Ke-qiang Lu",
  title =        "Hyperlink Classification: {A} New Approach to Improve
                 {PageRank}",
  crossref =     "Tjoa:2007:DIC",
  pages =        "274--277",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/DEXA.2007.14",
  ISBN =         "0-7695-2932-1, 0-7695-2932-1",
  ISBN-13 =      "978-0-7695-2932-5, 978-0-7695-2932-5",
  LCCN =         "QA76.9.D3",
  bibdate =      "Fri Feb 19 18:23:12 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4312900",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4312838",
}

@Article{Litvak:2007:DPW,
  author =       "N. Litvak and W. R. W. Scheinhardt and Y. Volkovich",
  title =        "{In-Degree} and {PageRank}: why do they follow similar
                 power laws?",
  journal =      j-INTERNET-MATH,
  volume =       "4",
  number =       "2-3",
  pages =        "175--198",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "62H99 (62E15 62E17 62P99 68M10)",
  MRnumber =     "MR2522875 (2010f:62177)",
  MRreviewer =   "Pranesh Kumar",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1243430605",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@InProceedings{Liu:2007:EBE,
  author =       "Maofu Liu and Wenjie Li and Mingli Wu and Hujun Hu",
  title =        "Event-Based Extractive Summarization Using Event
                 Semantic Relevance from External Linguistic Resource",
  crossref =     "Ock:2007:ASI",
  pages =        "117--122",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/ALPIT.2007.9",
  bibdate =      "Thu May 06 16:49:34 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@InProceedings{Liu:2007:KEU,
  author =       "Jianyi Liu and Jinghua Wang",
  title =        "Keyword Extraction Using Language Network",
  crossref =     "IEEE:2007:ICN",
  pages =        "129--134",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/NLPKE.2007.4368023",
  bibdate =      "Thu May 06 15:29:51 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Mason:2007:WMF,
  author =       "Zachary Mason",
  title =        "{WordRank}: {A} Method for Finding Search-Ad Keywords
                 for {Internet} Merchants",
  crossref =     "Clifton:2006:SIC",
  pages =        "12--12",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/ICIW.2007.73",
  bibdate =      "Thu May 06 16:27:14 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Melucci:2007:PWO,
  author =       "Massimo Melucci and Luca Pretto",
  editor =       "Giambattista Amati and Claudio Carpineto and Giovanni
                 Romano",
  booktitle =    "{Advances in information retrieval: 29th European
                 Conference on IR Research, ECIR 2007, Rome, Italy,
                 April 2-5, 2007: proceedings}",
  title =        "{PageRank}: when order changes",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "581--588",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1145/1060745.1060827",
  ISBN =         "3-540-71494-4",
  ISBN-13 =      "978-3-540-71494-1",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  abstract =     "As PageRank is a ranking algorithm, it is of prime
                 interest to study the order induced by its values on
                 webpages. In this paper a thorough mathematical
                 analysis of PageRank-induced order changes when the
                 damping factor varies is provided. Conditions that do
                 not allow variations in the order are studied, and the
                 mechanisms that make the order change are
                 mathematically investigated. Moreover the influence on
                 the order of a truncation in the actual computation of
                 PageRank through a power series is analysed.
                 Experiments carried out on a large Web digraph to
                 integrate the mathematical analysis show that PageRank
                 -- while working on a real digraph -- tends to hinder
                 variations in the order of large rankings, presenting a
                 high stability in its induced order both in the face of
                 large variations of the damping factor value and in the
                 face of truncations in its computation.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Mousavi:2007:CWU,
  author =       "H. Mousavi and M. E. Rafiei and A. Movaghar",
  title =        "Characterizing the {Web} Using a New Uniform Sampling
                 Approach",
  crossref =     "IEEE:2007:ICC",
  pages =        "1--5",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/COMSWA.2007.382558",
  bibdate =      "Thu May 06 16:46:35 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Nakakubo:2007:WPS,
  author =       "H. Nakakubo and S. Nakajima and K. Hatano and J.
                 Miyazaki and S. Uemura",
  title =        "{Web} Page Scoring Based on Link Analysis of {Web}
                 Page Sets",
  crossref =     "Tjoa:2007:DIC",
  pages =        "269--273",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/DEXA.2007.126",
  bibdate =      "Thu May 06 15:51:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Nan:2007:ENI,
  author =       "He Nan and Gan Wen-yan and Li De Yi",
  title =        "Evaluate Nodes Importance in the Network Using Data
                 Field Theory",
  crossref =     "Na:2007:IIC",
  pages =        "1225--1234",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/ICCIT.2007.88",
  bibdate =      "Thu May 06 16:40:05 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}

@InProceedings{Nie:2007:CSP,
  author =       "Lan Nie and Baoning Wu and Brian D. Davison",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceedings of
                 the 16th international conference on World Wide Web",
  title =        "A cautious surfer for {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "1119--1120",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1145/1149121.1149124",
  ISBN =         "1-59593-654-8",
  ISBN-13 =      "978-1-59593-654-7",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "This work proposes a novel cautious surfer to
                 incorporate trust into the process of calculating
                 authority for web pages. We evaluate a total of sixty
                 queries over two large, real-world datasets to
                 demonstrate that incorporating trust can improve
                 PageRank's performance.",
  acknowledgement = ack-nhfb,
  keywords =     "authority; ranking performance; spam; trust; web
                 search engine",
}

@Article{Pedroche:2007:MCP,
  author =       "Francisco Pedroche",
  title =        "Methods of calculating the {PageRank} vector",
  journal =      "Bol. Soc. Esp. Mat. Apl. S$\vec{\rm e}$MA",
  volume =       "39",
  pages =        "7--30",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1575-9822",
  MRclass =      "15A18 (65F10 65F15)",
  MRnumber =     "MR2406972 (2009c:15016)",
  MRreviewer =   "Juan Manuel Pe{\~n}a",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Bolet\'\i n de la Sociedad Espa\~nola de Matem\'atica
                 Aplicada. S$\vec{\rm e}$MA",
}

@InProceedings{Qiao:2007:EAP,
  author =       "Jonathan Qiao and Brittany Jones and Stacy Thrall",
  editor =       "Yong Shi and others",
  booktitle =    "Proceedings of the 7th international conference on
                 Computational Science, Part I: ICCS 2007",
  title =        "An Efficient Algorithm and Its Parallelization for
                 Computing {PageRank}",
  volume =       "4487--4490",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "237--244",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-72584-8_31",
  ISBN =         "3-540-72583-0",
  ISBN-13 =      "978-3-540-72583-1",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  bibdate =      "Sat May 8 18:33:07 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  abstract =     "In this paper, an efficient algorithm and its
                 parallelization to compute PageRank are proposed. There
                 are existing algorithms to perform such tasks. However,
                 some algorithms exclude dangling nodes which are an
                 important part and carry important information of the
                 web graph. In this work, we consider dangling nodes as
                 regular web pages without changing the web graph
                 structure and therefore fully preserve the information
                 carried by them. This differs from some other
                 algorithms which include dangling nodes but treat them
                 differently from regular pages for the purpose of
                 efficiency. We then give an efficient algorithm with
                 negligible overhead associated with dangling node
                 treatment. Moreover, the treatment poses little
                 difficulty in the parallelization of the algorithm.",
  acknowledgement = ack-nhfb,
  keywords =     "algorithm; dangling nodes; PageRank; power method",
}

@InProceedings{Rungsawang:2007:BLF,
  author =       "Arnon Rungsawang and Komthorn Puntumapon and Bundit
                 Manaskasemsak",
  booktitle =    "{AINA '07: 21st International Conference on Advanced
                 Information Networking and Applications (2007)}",
  title =        "Un-biasing the Link Farm Effect in {PageRank}
                 Computation",
  crossref =     "IEEE:2007:ICA",
  pages =        "924--931",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/AINA.2007.143",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4220990",
  abstract =     "Link analysis is a critical component of current
                 Internet search engines' results ranking software,
                 which determines the ordering of query results returned
                 to the user. The ordering of query results can have an
                 enormous impact on web traffic and the resulting
                 business activity of an enterprise; hence businesses
                 have a strong interest in having their web pages highly
                 ranked in search engine results. This has led to
                 attempts to artificially inflate page ranks by spamming
                 the link structure of the web. Building an artificial
                 condensed link structure called a 'link farm' is one
                 technique to influence a page ranking system, such as
                 the popular PageRank algorithm. In this paper, we
                 present an approach to remove the bias due to link
                 farms from PageRank computation. We propose a method to
                 first measure the PageRank weight accumulated by link
                 farms, and then distribute the weight to other web
                 pages by a modification of the transition matrix in the
                 standard PageRank algorithm. We present results of a
                 selected web graph that is manually spammed. The
                 results show that the proposed approach can effectively
                 reduce the bias from link farms in PageRank
                 computation.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4220856",
}

@InProceedings{Schatten:2007:OFS,
  author =       "M. Schatten and M. Zugaj",
  title =        "Organizing a Fishnet Structure",
  crossref =     "Luzar-Stiffler:2007:PII",
  pages =        "81--86",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/ITI.2007.4283748",
  bibdate =      "Thu May 06 15:05:59 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Shih:2007:VAR,
  author =       "Huang-Chia Shih and Chung-Lin Huang and Jenq-Neng
                 Hwang",
  title =        "Video Attention Ranking using Visual and Contextual
                 Attention Model for Content-based Sports Videos
                 Mining",
  crossref =     "IEEE:2007:IWM",
  pages =        "414--417",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/MMSP.2007.4412904",
  bibdate =      "Thu May 06 15:01:22 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Volkovich:2007:DFB,
  author =       "Yana Volkovich and Nelly Litvak and Debora Donato",
  title =        "Determining factors behind the {PageRank} log-log
                 plot",
  crossref =     "Bonato:2007:AMW",
  pages =        "108--123",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-77004-6_9",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "68U35 (05C90 68M10 68R10 91D30)",
  MRnumber =     "MR2504910",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  ZMnumber =     "1136.68339",
  acknowledgement = ack-nhfb,
}

@Article{Volkovich:2007:SMW,
  author =       "Y. Volkovich and D. Donato and N. Litvak",
  title =        "Stochastic models for {Web} ranking",
  journal =      j-SIGMETRICS,
  volume =       "35",
  number =       "3",
  pages =        "53--53",
  month =        dec,
  year =         "2007",
  CODEN =        "????",
  DOI =          "http://doi.acm.org/10.1145/1328690.1328713",
  ISSN =         "0163-5999 (print), 1557-9484 (electronic)",
  ISSN-L =       "0163-5999",
  bibdate =      "Fri Jun 27 09:42:53 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/sigmetrics.bib",
  abstract =     "Web search engines need to deal with hundreds and
                 thousands of pages which are relevant to a user's
                 query. Listing them in the right order is an important
                 and non-trivial task. Thus Google introduced {\em
                 PageRank\/} [1] as a popularity measure for Web pages.
                 Besides its primary application in search engines,
                 PageRank also became a major method for evaluating
                 importance of nodes in different informational networks
                 and database systems.",
  acknowledgement = ack-nhfb,
  fjournal =     "ACM SIGMETRICS Performance Evaluation Review",
  journal-URL =  "http://portal.acm.org/toc.cfm?id=J618",
}

@InProceedings{Wang:2007:KEB,
  author =       "Jinghua Wang and Jianyi Liu and Cong Wang",
  editor =       "Zhi-Hua Zhou and Hang Li and Qiang Yang",
  booktitle =    "{PPAKDD'07: Proceedings of the 11th Pacific-Asia
                 Conference on Advances in Knowledge Discovery and Data
                 Mining}",
  title =        "Keyword extraction based on {PageRank}",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "857--864",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.3115/1219044.1219064",
  ISBN =         "3-540-71700-5",
  ISBN-13 =      "978-3-540-71700-3",
  bibdate =      "Sat May 8 18:33:07 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNAI,
  abstract =     "Keywords are viewed as the words that represent the
                 topic and the content of the whole text. Keyword
                 extraction is an important technology in many areas of
                 document processing, such as text clustering, text
                 summarization, and text retrieval. This paper provides
                 a keyword extraction algorithm based on WordNet and
                 PageRank. Firstly, a text is represented as a rough
                 undirected weighted semantic graph with WordNet, which
                 defines synsets as vertices and relations of vertices
                 as edges, and assigns the weight of edges with the
                 relatedness of connected synsets. Then we apply
                 UW-PageRank in the rough graph to do word sense
                 disambiguation, prune the graph, and finally apply
                 UW-PageRank again on the pruned graph to extract
                 keywords. The experimental results show our algorithm
                 is practical and effective.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Wicks:2007:MEP,
  author =       "John R. Wicks and Amy Greenwald",
  editor =       "{ACM}",
  booktitle =    "Annual ACM Conference on Research and Development in
                 Information Retrieval Proceedings of the 30th annual
                 international ACM SIGIR conference on Research and
                 development in information retrieval",
  title =        "More efficient parallel computation of {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "861--862",
  year =         "2007",
  ISBN =         "1-59593-597-5",
  ISBN-13 =      "978-1-59593-597-7",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:11 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "",
  acknowledgement = ack-nhfb,
  keywords =     "pagerank; power iteration; web graph",
}

@InProceedings{Wicks:2007:PCP,
  author =       "John Wicks and Amy Greenwald",
  title =        "Parallelizing the computation of {PageRank}",
  crossref =     "Bonato:2007:AMW",
  pages =        "202--208",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-77004-6_17",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "68U35 (68M10 68R10 68W10)",
  MRnumber =     "MR2504918",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  ZMnumber =     "1136.68340",
  acknowledgement = ack-nhfb,
}

@Article{Wu:2007:PAA,
  author =       "Gang Wu and Yimin Wei",
  title =        "A Power-{Arnoldi} algorithm for computing {PageRank}",
  journal =      j-NUM-LIN-ALG-APPL,
  volume =       "14",
  number =       "7",
  pages =        "521--546",
  year =         "2007",
  CODEN =        "NLAAEM",
  DOI =          "http://dx.doi.org/10.1002/nla.531",
  ISSN =         "1070-5325 (print), 1099-1506 (electronic)",
  ISSN-L =       "1070-5325",
  MRclass =      "65F15; 65F15 65F10",
  MRnumber =     "MR2348401 (2009a:65097)",
  MRreviewer =   "Cristina Tablino Possio",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "05596057",
  acknowledgement = ack-nhfb,
  fjournal =     "Numerical Linear Algebra with Applications",
  journal-URL =  "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1506",
}

@InProceedings{Wu:2007:SAR,
  author =       "Gang Wu and Juanzi Li",
  title =        "{SWRank}: An Approach for Ranking {Semantic Web}
                 Reversely and Consistently",
  crossref =     "IEEE:2007:PTI",
  pages =        "116--121",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/SKG.2007.81",
  bibdate =      "Thu May 06 15:38:58 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Yang:2007:BBS,
  author =       "Lun Yang and Bin Wang and Gongli Xia and Zhenhua Xia
                 and Langlai Xu",
  booktitle =    "{BIC-TA 2007: Second International Conference on
                 Bio-Inspired Computing: Theories and Applications}",
  title =        "Bibliomics-based Selection of Analgesics Targets
                 through {Google}-{PageRank}-like Algorithm",
  crossref =     "IEEE:2007:SICa",
  pages =        "98--101",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/BICTA.2007.4806427",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4806427",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4801442",
}

@InProceedings{Yuan:2007:IPF,
  author =       "Fuyong Yuan and Chunxia Yin and Jian Liu",
  editor =       "{IEEE}",
  booktitle =    "{SNPD 2007: Eighth ACIS International Conference on
                 Software Engineering, Artificial Intelligence,
                 Networking, and Parallel\slash Distributed Computing}",
  title =        "Improvement of {PageRank} for Focused Crawler",
  volume =       "2",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "797--802",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/SNPD.2007.458",
  ISBN =         "0-7695-2909-7",
  ISBN-13 =      "978-0-7695-2909-7",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4287791",
  abstract =     "The rapid growth of the World-Wide Web poses
                 unprecedented scaling challenges for general-purpose
                 crawlers. Focused crawler is developed to collect
                 relevant web pages of interested topics form the
                 Internet. The PageRank algorithm is used in ranking web
                 pages. It estimates the page's authority by taking into
                 account the link structure of the Web. However, it
                 assigns each outlink the same weight and is independent
                 of topics, resulting in topic-drift. In this paper, we
                 proposed an improved PageRank algorithm, which we
                 called 'T-PageRank', and it based on 'topical random
                 surfer'. The experiment in focused crawler using the
                 T-PageRank has better performance than the Breath-first
                 and PageRank algorithms.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4287452",
  keywords =     "focused crawler; PageRank; T-PageRank; topical random
                 surfer",
}

@InProceedings{Yuan:2007:PFC,
  author =       "Fuyong Yuan and Chunxia Yin and Jian Liu",
  title =        "{PageRank} for Focused Crawler",
  crossref =     "Feng:2007:EAI",
  pages =        "797--802",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/SNPD.2007.458",
  bibdate =      "Fri Feb 19 18:09:30 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4287452",
}

@InProceedings{Yue:2007:UGM,
  author =       "BaoJun Yue and Heng Liang and Fengshan Bai",
  title =        "Understanding the {GeneRank} Model",
  crossref =     "IEEE:2007:BBE",
  pages =        "248--251",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/ICBBE.2007.67",
  bibdate =      "Thu May 06 16:52:48 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@InProceedings{Zhang:2007:AIP,
  author =       "Yulian Zhang and Chunxia Yin and Fuyong Yuan",
  booktitle =    "{FSKD 2007: Fourth International Conference on Fuzzy
                 Systems and Knowledge Discovery}",
  title =        "An Application of Improved {PageRank} in Focused
                 Crawler",
  crossref =     "Lei:2007:FPF",
  volume =       "2",
  pages =        "331--335",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/FSKD.2007.142",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4406097",
  abstract =     "The focused crawler of a special-purpose search engine
                 aims to selectively seek out pages that are relevant to
                 a pre-defined set of topics, rather than to exploit all
                 regions of the Web. The PageRank algorithm is often
                 used in ranking web pages, and it is also used in URL
                 ordering for focused crawler. It estimates the page's
                 authority by taking into account the link structure of
                 the Web. However, it assigns each outlink the same
                 weight and is independent of topics, resulting in
                 topic-drift. In this paper, we propose an improved
                 PageRank algorithm, which we called 'To-PageRank', and
                 then we present a crawling strategy using the
                 To-PageRank algorithm combining with the topic
                 similarity of the hyperlink metadata. The experiment in
                 focused crawler shows that the new improved crawling
                 strategy has better performance than the Breath-first
                 and PageRank algorithms.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4405868",
}

@InProceedings{Zhang:2007:SPW,
  author =       "Li Zhang and Tao Qin and Tie-Yan Liu and Ying Bao and
                 Hang Li",
  editor =       "Giambattista Amati and Claudio Carpineto and Giovanni
                 Romano",
  booktitle =    "{Advances in information retrieval: 29th European
                 Conference on IR Research, ECIR 2007, Rome, Italy,
                 April 2-5, 2007: proceedings}",
  title =        "{$N$}-step {PageRank} for {Web} search",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "653--660",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1145/324133.324140",
  ISBN =         "3-540-71494-4",
  ISBN-13 =      "978-3-540-71494-1",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  abstract =     "PageRank has been widely used to measure the
                 importance of web pages based on their interconnections
                 in the web graph. Mathematically speaking, PageRank can
                 be explained using a Markov random walk model, in which
                 only the direct outlinks of a page contribute to its
                 transition probability. In this paper, we propose
                 improving the PageRank algorithm by looking N -step
                 ahead when constructing the transition probability
                 matrix. The motivation comes from the similar 'looking
                 N -step ahead' strategy that is successfully used in
                 computer chess. Specifically, we assume that if the
                 random surfer knows the N -step outlinks of each web
                 page, he/she can make a better decision on choosing
                 which page to navigate for the next time. It is clear
                 that the classical PageRank algorithm is a special case
                 of our proposed N -step PageRank method. Experimental
                 results on the dataset of TREC Web track show that our
                 proposed algorithm can boost the search accuracy of
                 classical PageRank by more than 15\% in terms of mean
                 average precision.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Zhou:2007:CRA,
  author =       "Ding Zhou and S. A. Orshanskiy and Hongyuan Zha and C.
                 L. Giles",
  title =        "Co-ranking Authors and Documents in a Heterogeneous
                 Network",
  crossref =     "Ramakrishnan:2007:PSI",
  pages =        "739--744",
  year =         "2007",
  DOI =          "http://dx.doi.org/10.1109/ICDM.2007.57",
  bibdate =      "Fri May 07 17:05:21 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}

@Article{Andersen:2008:LCP,
  author =       "Reid Andersen and Christian Borgs and Jennifer Chayes
                 and John Hopcroft and Vahab Mirrokni and Shang-Hua Teng",
  title =        "Local computation of {PageRank} contributions",
  journal =      j-INTERNET-MATH,
  volume =       "5",
  number =       "1-2",
  pages =        "23--45",
  year =         "2008",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68R10 (05C85 68M11)",
  MRnumber =     "MR2560261",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1259158596",
  ZMnumber =     "1136.68316",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@Article{Andersen:2008:LPD,
  author =       "Reid Andersen and Fan Chung and Kevin Lang",
  title =        "Local partitioning for directed graphs using
                 {PageRank}",
  journal =      j-INTERNET-MATH,
  volume =       "5",
  number =       "1-2",
  pages =        "3--22",
  year =         "2008",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68R10 (05C20 05C70 68M11)",
  MRnumber =     "MR2560260",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1259158595",
  ZMnumber =     "1136.68317",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@InProceedings{Andersen:2008:RPL,
  author =       "Reid Andersen and Christian Borgs and Jennifer Chayes
                 and John Hopcroft and Kamal Jain and Vahab Mirrokni and
                 Shanghua Teng",
  editor =       "{ACM}",
  booktitle =    "AIRWeb; Vol. 295 Proceedings of the 4th international
                 workshop on Adversarial information retrieval on the
                 web",
  title =        "Robust {PageRank} and locally computable spam
                 detection features",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "69--76",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1145/1244408.1244413",
  ISBN =         "1-60558-159-3",
  ISBN-13 =      "978-1-60558-159-0",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Since the link structure of the web is an important
                 element in ranking systems on search engines, web
                 spammers widely use the link structure of the web to
                 increase the rank of their pages. Various link-based
                 features of web pages have been introduced and have
                 proven effective at identifying link spam. One
                 particularly successful family of features (as
                 described in the SpamRank algorithm), is based on
                 examining the sets of pages that contribute most to the
                 PageRank of a given vertex, called supporting sets. In
                 a recent paper, the current authors described an
                 algorithm for efficiently computing, for a single
                 specified vertex, an approximation of its supporting
                 sets. In this paper, we describe several link-based
                 spam-detection features, both supervised and
                 unsupervised, that can be derived from these
                 approximate supporting sets. In particular, we examine
                 the size of a node's supporting sets and the
                 approximate l 2 norm of the PageRank contributions from
                 other nodes. As a supervised feature, we examine the
                 composition of a node's supporting sets. We perform
                 experiments on two labeled real data sets to
                 demonstrate the effectiveness of these features for
                 spam detection, and demonstrate that these features can
                 be computed efficiently. Furthermore, we design a
                 variation of PageRank (called Robust PageRank) that
                 incorporates some of these features into its ranking,
                 argue that this variation is more robust against link
                 spam engineering, and give an algorithm for
                 approximating Robust PageRank.",
  acknowledgement = ack-nhfb,
  keywords =     "directed graphs; graph algorithms; link spam; local
                 algorithms; PageRank; unsupervised learning",
}

@PhdThesis{Augeri:2008:GIP,
  author =       "Christopher J. Augeri",
  title =        "On graph isomorphism and the {PageRank} algorithm",
  type =         "{Ph.D.} dissertation",
  school =       "Air Force Institute of Technology",
  address =      "Wright--Patterson Air Force Base, OH, USA",
  pages =        "xiv + 137",
  month =        sep,
  year =         "2008",
  ISBN =         "0-549-92090-0",
  ISBN-13 =      "978-0-549-92090-8",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "Order Number AAI3338375.",
  abstract =     "Graphs express relationships among objects, such as
                 the radio connectivity among nodes in unmanned vehicle
                 swarms. Some applications may rank a swarm's nodes by
                 their relative importance, for example, using the
                 PageRank algorithm applied in certain search engines to
                 order query responses. The PageRank values of the nodes
                 correspond to a unique eigenvector that can be computed
                 using the power method, an iterative technique based on
                 matrix multiplication. The first result is a practical
                 lower bound on the PageRank algorithm's execution time
                 that is derived by applying assumptions to the PageRank
                 perturbation scaling value and the PageRank vector's
                 required numerical precision. The second result
                 establishes nodes contained in the same block of the
                 graph's coarsest equitable partition must have equal
                 PageRank values. The third result, the AverageRank
                 algorithm, ensures such nodes receive equal PageRank
                 values. The fourth result, the ProductRank algorithm,
                 reduces the time needed to compute the PageRank vector
                 by eliminating certain dot products in the power method
                 if the graph's coarsest equitable partition contains
                 blocks composed of multiple vertices. The fifth result,
                 the QuotientRank algorithm, uses the quotient matrix
                 induced by the coarsest equitable partition to further
                 decrease the time needed to obtain a swarm's PageRank
                 vector. \par

                 The practical lower bound on the PageRank algorithm's
                 execution time was previously only suggested using
                 experimental results. The proof establishing vertices
                 contained in the same block of the graph's coarsest
                 equitable partition have equal PageRank values is based
                 on relating dot products and Weisfeiler-Lehman
                 stabilization, a much different approach than applied
                 in an existing proof. The existing proof was also
                 extended to show the quotient matrix could be used to
                 reduce the PageRank algorithm's execution time.
                 However, its authors did not develop an algorithm or
                 analyze its execution time bounds. These results
                 motivate many avenues of future research related to
                 graph isomorphism and linear algebra.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Avrachenkov:2008:PBC,
  author =       "Konstantin Avrachenkov and Vladimir Dobrynin and Danil
                 Nemirovsky and Son Kim Pham and Elena Smirnova",
  editor =       "{ACM}",
  booktitle =    "Proceedings of the 31st Annual International ACM SIGIR
                 Conference on Research and Development in Information
                 Retrieval",
  title =        "{PageRank} based clustering of hypertext document
                 collections",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "873--874",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1145/511446.511513",
  ISBN =         "1-60558-164-X",
  ISBN-13 =      "978-1-60558-164-4",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:05 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Clustering hypertext document collection is an
                 important task in Information Retrieval. Most
                 clustering methods are based on document content and do
                 not take into account the hyper-text links. Here we
                 propose a novel PageRank based clustering (PRC)
                 algorithm which uses the hypertext structure. The PRC
                 algorithm produces graph partitioning with high
                 modularity and coverage. The comparison of the PRC
                 algorithm with two content based clustering algorithms
                 shows that there is a good match between PRC clustering
                 and content based clustering.",
  acknowledgement = ack-nhfb,
  keywords =     "directed graphs; PageRank based clustering",
}

@Article{Avrachenkov:2008:SPA,
  author =       "Konstantin Avrachenkov and Nelly Litvak and Kim Son
                 Pham",
  title =        "A singular perturbation approach for choosing the
                 {PageRank} damping factor",
  journal =      j-INTERNET-MATH,
  volume =       "5",
  number =       "1-2",
  pages =        "47--69",
  year =         "2008",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68R10 (05C82 68M11)",
  MRnumber =     "MR2560262",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1259158597",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
}

@InProceedings{Bar-Yossef:2008:LAPa,
  author =       "Ziv Bar-Yossef and Li-Tal Mashiach",
  editor =       "{ACM}",
  booktitle =    "Proceedings of the 31st annual international ACM SIGIR
                 conference on Research and development in information
                 retrieval",
  title =        "Local approximation of {PageRank} and reverse
                 {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "865--866",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1145/1031171.1031248",
  ISBN =         "1-60558-164-X",
  ISBN-13 =      "978-1-60558-164-4",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We consider the problem of approximating the PageRank
                 of a target node using only local information provided
                 by a link server. We prove that local approximation of
                 PageRank is feasible if and only if the graph has low
                 in-degree and admits fast PageRank convergence. While
                 natural graphs, such as the web graph, are abundant
                 with high in-degree nodes, making local PageRank
                 approximation too costly, we show that reverse natural
                 graphs tend to have low in degree while maintaining
                 fast PageRank convergence. It follows that calculating
                 Reverse PageRank locally is frequently more feasible
                 than computing PageRank locally. Finally, we
                 demonstrate the usefulness of Reverse PageRank in five
                 different applications.",
  acknowledgement = ack-nhfb,
  keywords =     "local approximation; lower bounds; PageRank; reverse
                 PageRank",
}

@InProceedings{Bar-Yossef:2008:LAPb,
  author =       "Ziv Bar-Yossef and Li-Tal Mashiach",
  editor =       "{ACM}",
  booktitle =    "Conference on Information and Knowledge Management
                 Proceeding of the 17th ACM conference on Information
                 and knowledge management",
  title =        "Local approximation of {PageRank} and {Reverse
                 PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "279--288",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1016/0890-5401(89)90067-9",
  ISBN =         "1-59593-991-1",
  ISBN-13 =      "978-1-59593-991-3",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We consider the problem of approximating the PageRank
                 of a target node using only local information provided
                 by a link server. This problem was originally studied
                 by Chen, Gan, and Suel (CIKM 2004), who presented an
                 algorithm for tackling it. We prove that local
                 approximation of PageRank, even to within modest
                 approximation factors, is infeasible in the worst-case,
                 as it requires probing the link server for $\Omega$(n)
                 nodes, where n is the size of the graph. The difficulty
                 emanates from nodes of high in-degree and/or from slow
                 convergence of the PageRank random walk. \par

                 We show that when the graph has bounded in-degree and
                 admits fast PageRank convergence, then local PageRank
                 approximation can be done using a small number of
                 queries. Unfortunately, natural graphs, such as the web
                 graph, are abundant with high in-degree nodes, making
                 this algorithm (or any other local approximation
                 algorithm) too costly. On the other hand, reverse
                 natural graphs tend to have low in-degree while
                 maintaining fast PageRank convergence. It follows that
                 calculating Reverse PageRank locally is frequently more
                 feasible than computing PageRank locally. \par

                 We demonstrate that Reverse PageRank is useful for
                 several applications, including computation of hub
                 scores for web pages, finding influencers in social
                 networks, obtaining good seeds for crawling, and
                 measurement of semantic relatedness between concepts in
                 a taxonomy.",
  acknowledgement = ack-nhfb,
  keywords =     "local approximation; lower bounds; pagerank; reverse
                 pagerank",
}

@InProceedings{Bauckhage:2008:ITU,
  author =       "Christian Bauckhage",
  editor =       "Gerhard Rigoll",
  booktitle =    "Proceedings of the 30th DAGM Symposium on Pattern
                 Recognition",
  title =        "Image Tagging Using {PageRank} over Bipartite Graphs",
  volume =       "5096",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "426--435",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-69321-5_43",
  ISBN =         "3-540-69320-3",
  ISBN-13 =      "978-3-540-69320-8",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "TA1650 .D35 2008",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  abstract =     "We consider the problem of automatic image tagging for
                 online services and explore a prototype-based approach
                 that applies ideas from manifold ranking. Since
                 algorithms for ranking on graphs or manifolds often
                 lack a way of dealing with out of sample data, they are
                 of limited use for pattern recognition. In this paper,
                 we therefore propose to consider diffusion processes
                 over bipartite graphs which allow for a dual treatment
                 of objects and features. As with Google's PageRank,
                 this leads to Markov processes over the prototypes. In
                 contrast to related methods, our model provides a
                 Bayesian interpretation of the transition matrix and
                 enables the ranking and consequently the classification
                 of unknown entities. By design, the method is tailored
                 to histogram features and we apply it to
                 histogram-based color image analysis. Experiments with
                 images downloaded from flickr.com illustrate object
                 localization in realistic scenes.",
  acknowledgement = ack-nhfb,
}

@Article{Bini:2008:ESP,
  author =       "Dario A. Bini and Gianna M. {Del Corso} and Francesco
                 Romani",
  title =        "Evaluating scientific products by means of
                 citation-based models: a first analysis and
                 validation",
  journal =      j-ELECTRON-TRANS-NUMER-ANAL,
  volume =       "33",
  pages =        "1--16",
  year =         "2008\slash 2009",
  CODEN =        "????",
  ISSN =         "1068-9613 (print), 1097-4067 (electronic)",
  ISSN-L =       "1068-9613",
  bibdate =      "Mon Sep 6 12:28:30 MDT 2010",
  bibsource =    "http://etna.mcs.kent.edu/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://etna.mcs.kent.edu/vol.33.2008-2009/pp1-16.dir/pp1-16.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "Electronic Transactions on Numerical Analysis",
}

@InProceedings{Boldi:2008:TPT,
  author =       "Paolo Boldi and Roberto Posenato and Massimo Santini
                 and Sebastiano Vigna",
  title =        "Traps and Pitfalls of Topic-Biased {PageRank}",
  crossref =     "Aiello:2008:AMW",
  pages =        "107--116",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-78808-9_10",
  ISBN =         "3-540-78807-7",
  ISBN-13 =      "978-3-540-78807-2",
  LCCN =         "????",
  MRclass =      "68M10 68R10 68U35",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1142.68309",
  abstract =     "We discuss a number of issues in the definition,
                 computation and comparison of PageRank values that have
                 been addressed sparsely in the literature, often with
                 contradictory approaches. We study the difference
                 between weakly and strongly preferential PageRank,
                 which patch the dangling nodes with different
                 distributions, extending analytical formulae known for
                 the strongly preferential case, and corroborating our
                 results with experiments on a snapshot of 100 millions
                 of pages of the {\tt .uk} domain. The experiments show
                 that the two PageRank versions are poorly correlated,
                 and results about each one cannot be blindly applied to
                 the other; moreover, our computations highlight some
                 new concerns about the usage of exchange-based
                 correlation indices (such as Kendall's $\tau$) on
                 approximated rankings.",
  acknowledgement = ack-nhfb,
}

@Article{Brezinski:2008:REP,
  author =       "C. Brezinski and M. Redivo-Zaglia",
  title =        "Rational extrapolation for the {PageRank} vector",
  journal =      j-MATH-COMPUT,
  volume =       "77",
  number =       "263",
  pages =        "1585--1598",
  month =        jul,
  year =         "2008",
  CODEN =        "MCMPAF",
  DOI =          "http://dx.doi.org/10.1090/S0025-5718-08-02086-3",
  ISSN =         "0025-5718 (paper), 1088-6842 (electronic)",
  ISSN-L =       "0025-5718",
  MRclass =      "68U35 (65F15)",
  MRnumber =     "MR2398781 (2009d:68171)",
  MRreviewer =   "Stefano Serra Capizzano",
  bibdate =      "Tue Jul 8 06:24:30 MDT 2008",
  bibsource =    "http://www.ams.org/mcom/2008-77-263;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/mathcomp2000.bib;
                 http://www.ams.org/mcom/2008-77-263/S0025-5718-08-02086-3/home.html;
                 http://www.ams.org/mcom/2008-77-263/S0025-5718-08-02086-3/S0025-5718-08-02086-3.dvi;
                 http://www.ams.org/mcom/2008-77-263/S0025-5718-08-02086-3/S0025-5718-08-02086-3.pdf;
                 http://www.ams.org/mcom/2008-77-263/S0025-5718-08-02086-3/S0025-5718-08-02086-3.ps",
  acknowledgement = ack-nhfb,
  fjournal =     "Mathematics of Computation",
  journal-URL =  "http://www.ams.org/mcom/",
}

@InProceedings{Chebolu:2008:PRS,
  author =       "Prasad Chebolu and P{\'a}ll Melsted",
  title =        "{PageRank} and the random surfer model",
  crossref =     "ACM:2008:PNA",
  pages =        "1010--1018",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1145/316188.316229",
  MRclass =      "68R10 (05C80 68P20 68U99)",
  MRnumber =     "MR2487672",
  bibdate =      "Sat May 8 18:33:11 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In recent years there has been considerable interest
                 in analyzing random graph models for the Web. We
                 consider two such models --- the Random Surfer model,
                 introduced by Blum et al. [7], and the PageRank-based
                 selection model, proposed by Pandurangan et al. [18].
                 It has been observed that search engines influence the
                 growth of the Web. The PageRank-based selection model
                 tries to capture the effect that these search engines
                 have on the growth of the Web by adding new links
                 according to Pagerank. The PageRank algorithm is used
                 in the Google search engine [1] for ranking search
                 results. \par

                 We show the equivalence of the two random graph models
                 and carry out the analysis in the Random Surfer model,
                 since it is easier to work with. We analyze the
                 expected in-degree of vertices and show that it follows
                 a powerlaw. We also analyze the expected PageRank of
                 vertices and show that it follows the same powerlaw as
                 the expected degree. \par

                 We show that in both models the expected degree and the
                 PageRank of the first vertex, the root of the graph,
                 follow the same powerlaw. However, the power undergoes
                 a phase-transition as we vary the parameter of the
                 model. This peculiar behavior of the root has not been
                 observed in previous analysis and simulations of the
                 two models.",
  acknowledgement = ack-nhfb,
}

@Article{deKerchove:2008:MPO,
  author =       "Cristobald de Kerchove and Laure Ninove and Paul van
                 Dooren",
  title =        "Maximizing {PageRank} via outlinks",
  journal =      j-LINEAR-ALGEBRA-APPL,
  volume =       "429",
  number =       "5--6",
  pages =        "1254--1276",
  day =          "1",
  month =        sep,
  year =         "2008",
  CODEN =        "LAAPAW",
  DOI =          "http://dx.doi.org/10.1016/j.laa.2008.01.023",
  ISSN =         "0024-3795 (print), 1873-1856 (electronic)",
  ISSN-L =       "0024-3795",
  MRclass =      "15A18 (15A51 15A57 60J10 68U35)",
  MRnumber =     "MR2433177 (2009e:15030)",
  MRreviewer =   "Thomas H. Foregger",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.sciencedirect.com/science/journal/00243795",
  ZMnumber =     "1147.68387",
  acknowledgement = ack-nhfb,
  fjournal =     "Linear Algebra and its Applications",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00243795",
}

@Article{DeSterck:2008:MAA,
  author =       "H. {De Sterck} and Thomas A. Manteuffel and Stephen F.
                 McCormick and Quoc Nguyen and John Ruge",
  title =        "Multilevel Adaptive Aggregation for {Markov} Chains,
                 with Application to {Web} Ranking",
  journal =      j-SIAM-J-SCI-COMP,
  volume =       "30",
  number =       "5",
  pages =        "2235--2262",
  month =        "????",
  year =         "2008",
  CODEN =        "SJOCE3",
  DOI =          "http://dx.doi.org/10.1137/070685142",
  ISSN =         "1064-8275 (print), 1095-7197 (electronic)",
  ISSN-L =       "1064-8275",
  bibdate =      "Wed May 19 10:44:08 MDT 2010",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SISC/30/5;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "A multilevel adaptive aggregation method for
                 calculating the stationary probability vector of an
                 irreducible stochastic matrix is described. The method
                 is a special case of the adaptive smoothed aggregation
                 and adaptive algebraic multigrid methods for sparse
                 linear systems and is also closely related to certain
                 extensively studied iterative
                 aggregation/disaggregation methods for Markov chains.
                 In contrast to most existing approaches, our
                 aggregation process does not employ any explicit
                 advance knowledge of the topology of the Markov chain.
                 Instead, adaptive agglomeration is proposed that is
                 based on the strength of connection in a scaled problem
                 matrix, in which the columns of the original problem
                 matrix at each recursive fine level are scaled with the
                 current probability vector iterate at that level. The
                 strength of connection is determined as in the
                 algebraic multigrid method, and the aggregation process
                 is fully adaptive, with optimized aggregates chosen in
                 each step of the iteration and at all recursive levels.
                 The multilevel method is applied to a set of stochastic
                 matrices that provide models for web page ranking.
                 Numerical tests serve to illustrate for which types of
                 stochastic matrices the multilevel adaptive method may
                 provide significant speedup compared to standard
                 iterative methods. The tests also provide more insight
                 into why Google's PageRank model is a successful model
                 for determining a ranking of web pages.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Scientific Computing",
  journal-URL =  "http://epubs.siam.org/sisc",
}

@InProceedings{Fortunato:2008:APD,
  author =       "Santo Fortunato and Mari{\'a}n Bogu{\~n}{\'a} and
                 Alessandro Flammini and Filippo Menczer",
  title =        "Approximating {PageRank} from In-Degree",
  crossref =     "Aiello:2008:AMW",
  pages =        "59--71",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-78808-9_6",
  ISBN =         "3-540-78807-7",
  ISBN-13 =      "978-3-540-78807-2",
  LCCN =         "????",
  MRclass =      "68M10 68U35 68P20",
  bibdate =      "Sat May 8 18:33:11 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1142.68311",
  abstract =     "PageRank is a key element in the success of search
                 engines, allowing to rank the most important hits in
                 the top screen of results. One key aspect that
                 distinguishes PageRank from other prestige measures
                 such as in-degree is its global nature. From the
                 information provider perspective, this makes it
                 difficult or impossible to predict how their pages will
                 be ranked. Consequently a market has emerged for the
                 optimization of search engine results. Here we study
                 the accuracy with which PageRank can be approximated by
                 in-degree, a local measure made freely available by
                 search engines. Theoretical and empirical analyses lead
                 to conclude that given the weak degree correlations in
                 the Web link graph, the approximation can be relatively
                 accurate, giving service and information providers an
                 effective new marketing tool.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Guo:2008:IBM,
  author =       "Chonghui Guo and Liang Zhang",
  editor =       "{IEEE}",
  booktitle =    "{WiCOM '08. 4th International Conference on (Online)
                 Wireless Communications, Networking and Mobile
                 Computing, Dalian, China, 12--17 October 2008}",
  title =        "An Improved {BA} Model Based on the {PageRank}
                 Algorithm",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "1--4",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/WiCom.2008.2675",
  ISBN =         "1-4244-2107-1",
  ISBN-13 =      "978-1-4244-2107-7",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4680864",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4677908",
}

@InProceedings{Gupta:2008:FAT,
  author =       "Manish Gupta and Amit Pathak and Soumen Chakrabarti",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceeding of
                 the 17th international conference on World Wide Web",
  title =        "Fast algorithms for top-$k$ personalized {PageRank}
                 queries",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "1225--1226",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1145/775152.775191",
  ISBN =         "1-60558-085-6",
  ISBN-13 =      "978-1-60558-085-2",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In entity-relation (ER) graphs $(V,E)$, nodes $V$
                 represent typed entities and edges $E$ represent typed
                 relations. For dynamic personalized PageRank queries,
                 nodes are ranked by their steady-state probabilities
                 obtained using the standard random surfer model. In
                 this work, we propose a framework to answer top-$k$
                 graph conductance queries. Our top-$k$ ranking
                 technique leads to a 4$\times$ speedup, and overall,
                 our system executes queries 200-1600$\times$ faster
                 than whole-graph PageRank. Some queries might contain
                 hard predicates i.e. predicates that must be satisfied
                 by the answer nodes. E.g. we may seek authoritative
                 papers on public key cryptography, but only those
                 written during 1997. We extend our system to handle
                 hard predicates. Our system achieves these substantial
                 query speedups while consuming only 10--20\% of the
                 space taken by a regular text index.",
  acknowledgement = ack-nhfb,
  keywords =     "HubRank; node-deletion; pagerank; personalized;
                 top-$k$",
}

@Article{Hristidis:2008:ABK,
  author =       "Vagelis Hristidis and Heasoo Hwang and Yannis
                 Papakonstantinou",
  title =        "Authority-based keyword search in databases",
  journal =      j-TODS,
  volume =       "33",
  number =       "1",
  pages =        "1:1--1:??",
  month =        mar,
  year =         "2008",
  CODEN =        "ATDSD3",
  DOI =          "http://doi.acm.org/10.1145/1331904.1331905",
  ISSN =         "0362-5915 (print), 1557-4644 (electronic)",
  ISSN-L =       "0362-5915",
  bibdate =      "Thu Jun 12 16:37:49 MDT 2008",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tods/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/tods.bib",
  abstract =     "Our system applies authority-based ranking to keyword
                 search in databases modeled as labeled graphs. Three
                 ranking factors are used: the relevance to the query,
                 the specificity and the importance of the result. All
                 factors are handled using authority-flow techniques
                 that exploit the link-structure of the data graph, in
                 contrast to traditional Information Retrieval. We
                 address the performance challenges in computing the
                 authority flows in databases by using precomputation
                 and exploiting the database schema if present. We
                 conducted user surveys and performance experiments on
                 multiple real and synthetic datasets, to assess the
                 semantic meaningfulness and performance of our
                 system.",
  acknowledgement = ack-nhfb,
  articleno =    "1",
  fjournal =     "ACM Transactions on Database Systems",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J777",
  keywords =     "Authority flow; PageRank; quality experiments;
                 ranking; specificity",
}

@Article{Ipsen:2008:PCS,
  author =       "Ilse C. F. Ipsen and Teresa M. Selee",
  title =        "{PageRank} Computation, with Special Attention to
                 Dangling Nodes",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "29",
  number =       "4",
  pages =        "1281--1296",
  month =        "????",
  year =         "2008",
  CODEN =        "SJMAEL",
  DOI =          "http://dx.doi.org/10.1137/060664331",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  bibdate =      "Tue May 18 22:32:22 MDT 2010",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toclist/SIMAX/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
}

@InProceedings{Ishii:2008:DRAa,
  author =       "H. Ishii and R. Tempo",
  booktitle =    "{CDC 2008: 47th IEEE Conference on Decision and
                 Control}",
  title =        "A distributed randomized approach for the {PageRank}
                 computation: {Part 1}",
  crossref =     "IEEE:2008:ICD",
  pages =        "3523--3528",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/CDC.2008.4739020",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4739020",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4721212",
}

@InProceedings{Ishii:2008:DRAb,
  author =       "H. Ishii and R. Tempo",
  title =        "A distributed randomized approach for the {PageRank}
                 computation: {Part 2}",
  crossref =     "IEEE:2008:ICD",
  pages =        "3529--3534",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/CDC.2008.4739022",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4739022",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4721212",
}

@InProceedings{Jing:2008:PPI,
  author =       "Yushi Jing and Shumeet Baluja",
  editor =       "{ACM}",
  booktitle =    "International World Wide Web Conference Proceeding of
                 the 17th international conference on World Wide Web",
  title =        "{PageRank} for product image search",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "307--316",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1023/B:VISI.0000013087.49260.fb",
  ISBN =         "1-60558-085-6",
  ISBN-13 =      "978-1-60558-085-2",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In this paper, we cast the image-ranking problem into
                 the task of identifying 'authority' nodes on an
                 inferred visual similarity graph and propose an
                 algorithm to analyze the visual link structure that can
                 be created among a group of images. Through an
                 iterative procedure based on the PageRank computation,
                 a numerical weight is assigned to each image; this
                 measures its relative importance to the other images
                 being considered. The incorporation of visual signals
                 in this process differs from the majority of
                 large-scale commercial-search engines in use today.
                 Commercial search-engines often solely rely on the text
                 clues of the pages in which images are embedded to rank
                 images, and often entirely ignore the content of the
                 images themselves as a ranking signal. To quantify the
                 performance of our approach in a real-world system, we
                 conducted a series of experiments based on the task of
                 retrieving images for 2000 of the most popular products
                 queries. Our experimental results show significant
                 improvement, in terms of user satisfaction and
                 relevancy, in comparison to the most recent Google
                 Image Search results.",
  acknowledgement = ack-nhfb,
  keywords =     "graph theory; pagerank; visual similarity",
}

@Article{Jing:2008:VAP,
  author =       "Yushi Jing and S. Baluja",
  title =        "{VisualRank}: Applying {PageRank} to Large-Scale Image
                 Search",
  journal =      j-IEEE-TRANS-PATT-ANAL-MACH-INTEL,
  volume =       "30",
  number =       "11",
  pages =        "1877--1890",
  month =        "????",
  year =         "2008",
  CODEN =        "ITPIDJ",
  DOI =          "http://dx.doi.org/10.1109/TPAMI.2008.121",
  ISSN =         "0162-8828",
  ISSN-L =       "0162-8828",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4522561",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34",
  fjournal =     "IEEE Transactions on Pattern Analysis and Machine Intelligence",
  journal-URL =  "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34",
}

@InProceedings{Kale:2008:DRE,
  author =       "M. Kale and P. S. Thilagam",
  booktitle =    "{ICCSIT '08: International Conference on Computer
                 Science and Information Technology}",
  title =        "{DYNA-RANK}: Efficient Calculation and Updation of
                 {PageRank}",
  crossref =     "IEEE:2008:PIC",
  pages =        "808--812",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/ICCSIT.2008.118",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4624979",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4624812",
}

@Article{Kaplan:2008:BRB,
  author =       "Daniel T. Kaplan",
  title =        "Book Review: {{{\booktitle{Google}'s PageRank and
                 Beyond: The Science of Search Engine Rankings}, by {Amy
                 N. Langville; Carl D. Meyer}}}",
  journal =      j-AMER-MATH-MONTHLY,
  volume =       "115",
  number =       "8",
  pages =        "765--768",
  month =        oct,
  year =         "2008",
  CODEN =        "AMMYAE",
  ISSN =         "0002-9890 (print), 1930-0972 (electronic)",
  ISSN-L =       "0002-9890",
  bibdate =      "Mon Jan 30 12:00:31 MST 2012",
  bibsource =    "http://www.jstor.org/journals/00029890.html;
                 http://www.jstor.org/stable/i27642579;
                 http://www.math.utah.edu/pub/tex/bib/amermathmonthly2000.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.jstor.org/stable/27642602",
  acknowledgement = ack-nhfb,
  fjournal =     "American Mathematical Monthly",
  journal-URL =  "http://www.jstor.org/page/journal/amermathmont/about.html",
}

@InProceedings{Li:2008:APA,
  author =       "Fagui Li and Tong Yi",
  editor =       "{IEEE}",
  booktitle =    "{PACIIA '08: Pacific-Asia Workshop on Computational
                 Intelligence and Industrial Application (2008)}",
  title =        "Apply {PageRank} Algorithm to Measuring Relationship's
                 Complexity",
  volume =       "1",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "914--917",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/PACIIA.2008.309",
  ISBN =         "0-7695-3490-2",
  ISBN-13 =      "978-0-7695-3490-9",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4756692",
  abstract =     "Software measurement can help software developers
                 analyze reliability, maintainability and complexity of
                 systems. Till now, researchers have proposed lots of
                 metrics for UML class diagrams range from cohesion to
                 couple. However very little work is involved in
                 measuring weights of relationships. This paper
                 describes how to measure weights of relationships
                 objectively and mechanically, in which famous PageRank
                 algorithm in web structure mining is used. Finally, a
                 small but realistic example is illustrated.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4756503",
  keywords =     "pagerank algorithm; software measurement; unified
                 modeling language",
}

@Article{Lin:2008:PHR,
  author =       "Jimmy Lin",
  title =        "{PageRank} without hyperlinks: Reranking with {PubMed}
                 related article networks for biomedical text
                 retrieval",
  journal =      "BMC Bioinformatics",
  volume =       "9",
  pages =        "270--??",
  year =         "2008",
  CODEN =        "BBMIC4",
  DOI =          "http://dx.doi.org/10.1186/1471-2105-9-270",
  ISSN =         "1471-2105",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 10:03:23 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.biomedcentral.com/1471-2105/9/270",
  abstract =     "Graph analysis algorithms such as PageRank and HITS
                 have been successful in Web environments because they
                 are able to extract important inter-document
                 relationships from manually-created hyperlinks. We
                 consider the application of these techniques to
                 biomedical text retrieval. In the current PubMed search
                 interface, a MEDLINE citation is connected to a number
                 of related citations, which are in turn connected to
                 other citations. Thus, a MEDLINE record represents a
                 node in a vast content-similarity network. This article
                 explores the hypothesis that these networks can be
                 exploited for text retrieval, in the same manner as
                 hyperlink graphs on the Web.",
  acknowledgement = ack-nhfb,
  ajournal =     "BMC Bioinf.",
  keywords =     "BioMed Central (BMC)",
}

@InProceedings{Litvak:2008:PRB,
  author =       "Nelly Litvak and Werner R. W. Scheinhardt and Yana
                 Volkovich",
  title =        "Probabilistic relation between in-degree and
                 {PageRank}",
  crossref =     "Aiello:2008:AMW",
  pages =        "72--83",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-78808-9_7",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "68M10 (05C90 37A50 68P20)",
  MRnumber =     "MR2473494 (2010c:68014)",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  ZMnumber =     "1142.68314",
  abstract =     "This paper presents a novel stochastic model that
                 explains the relation between power laws of In-Degree
                 and PageRank. PageRank is a popularity measure designed
                 by Google to rank Web pages. We model the relation
                 between PageRank and In-Degree through a stochastic
                 equation, which is inspired by the original definition
                 of PageRank. Using the theory of regular variation and
                 Tauberian theorems, we prove that the tail
                 distributions of PageRank and In-Degree differ only by
                 a multiplicative constant, for which we derive a
                 closed-form expression. Our analytical results are in
                 good agreement with Web data.",
  acknowledgement = ack-nhfb,
  keywords =     "Algorithms; Experimentation; In-Degree; PageRank;
                 Power law; Regular variation; Stochastic equation;
                 Theory; Verification; Web measurement",
}

@InProceedings{Liu:2008:PPB,
  author =       "Yong Liu and Xiaolei Wang and Jin Zhang and Hongbo
                 Xu",
  editor =       "{IEEE}",
  booktitle =    "{WSCS '08: IEEE International Workshop on Semantic
                 Computing and Systems (2008)}",
  title =        "Personalized {PageRank} Based Multi-document
                 Summarization",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "169--173",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/WSCS.2008.32",
  ISBN =         "0-7695-3316-7",
  ISBN-13 =      "978-0-7695-3316-2",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4570834",
  abstract =     "This paper presents a novel multi-document
                 summarization approach based on Personalized PageRank
                 (PPRSum). In this algorithm, we uniformly integrate
                 various kinds of information in the corpus. At first,
                 we train a salience model of sentence global features
                 based on Na{\"\i}ve Bayes Model. Secondly, we generate
                 a relevance model for each corpus utilizing the query
                 of it. Then, we compute the personalized prior
                 probability for each sentence in the corpus utilizing
                 the salience model and the relevance model both. With
                 the help of personalized prior probability, a
                 Personalized PageRank ranking process is performed
                 depending on the relationships among all sentences in
                 the corpus. Additionally, the redundancy penalty is
                 imposed on each sentence. The summary is produced by
                 choosing the sentences with both high query-focused
                 information richness and high information novelty.
                 Experiments on DUC2007 are performed and the ROUGE
                 evaluation results show that PPRSum ranks between the
                 1st and the 2nd systems on DUC2007 main task.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4570797",
  keywords =     "Personalized PageRank; Na{\"\i}ve Bayes model;
                 personalized prior probability",
}

@Article{Ma:2008:BPC,
  author =       "Nan Ma and Jiancheng Guan and Yi Zhao",
  title =        "Bringing {PageRank} to the citation analysis",
  journal =      "Information Processing and Management: an
                 International Journal",
  volume =       "44",
  number =       "2",
  pages =        "800--810",
  month =        mar,
  year =         "2008",
  CODEN =        "????",
  DOI =          "http://dx.doi.org/10.1145/324133.324140",
  ISSN =         "????",
  bibdate =      "Sat May 8 18:33:04 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The paper attempts to provide an alternative method
                 for measuring the importance of scientific papers based
                 on the Google's PageRank. The method is a meaningful
                 extension of the common integer counting of citations
                 and is then experimented for bringing PageRank to the
                 citation analysis in a large citation network. It
                 offers a more integrated picture of the publications'
                 influence in a specific field. We firstly calculate the
                 PageRanks of scientific papers. The distributional
                 characteristics and comparison with the traditionally
                 used number of citations are then analyzed in detail.
                 Furthermore, the PageRank is implemented in the
                 evaluation of research influence for several countries
                 in the field of Biochemistry and Molecular Biology
                 during the time period of 2000-2005. Finally, some
                 advantages of bringing PageRank to the citation
                 analysis are concluded.",
  acknowledgement = ack-nhfb,
  keywords =     "citation analysis; citation network; internal
                 citations; PageRank",
}

@InProceedings{McGettrick:2008:FAP,
  author =       "S. McGettrick and D. Geraghty and C. McElroy",
  editor =       "Udo Kebschull and Marco Platzner and J{\"u}rgen
                 Teich",
  booktitle =    "{FPL 2008: International Conference on
                 Field-Programmable Logic and Applications: Heidelberg,
                 Germany, September 8--10, 2008}",
  title =        "An {FPGA} architecture for the {PageRank} eigenvector
                 problem",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "523--526",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/FPL.2008.4629999",
  ISBN =         "1-4244-1961-1, 1-4244-1960-3 (set)",
  ISBN-13 =      "978-1-4244-1961-6, 978-1-4244-1960-9 (set)",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "IEEE catalog number CFP08623.",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4629999",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4625340",
}

@InProceedings{McGettrick:2008:TFS,
  author =       "S{\'e}amas McGettrick and Dermot Geraghty and
                 Ciar{\'a}n McElroy",
  editor =       "Christian Bischof and others",
  booktitle =    "Parallel computing: Architectures, algorithms and
                 applications. Selected papers based on the
                 presentations at the international parallel computing
                 conference (ParCo 2007), Aachen, Germany, September
                 4--7, 2007",
  title =        "Towards an {FPGA} solver for the {PageRank}
                 eigenvector problem",
  volume =       "15",
  publisher =    pub-IOS,
  address =      pub-IOS:adr,
  pages =        "793--800",
  year =         "2008",
  ISBN =         "1-58603-796-X",
  ISBN-13 =      "978-1-58603-796-3",
  LCCN =         "????",
  MRclass =      "68M10 65F30 65Y10 65Y20",
  bibdate =      "Thu May 06 11:31:36 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Advances in Parallel Computing",
  ZMnumber =     "1160.68317",
  acknowledgement = ack-nhfb,
}

@Article{Pan:2008:APA,
  author =       "Hao Pan and Long-Yuan Tan",
  title =        "Adaptive {PageRank} algorithm search strategy for
                 specific topics",
  journal =      "J. Comput. Appl.",
  volume =       "28",
  number =       "9",
  pages =        "2192--2194",
  year =         "2008",
  CODEN =        "????",
  ISSN =         "????",
  MRclass =      "68M11 68M10 68P10",
  bibdate =      "Thu May 06 11:29:26 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1180.68039",
  acknowledgement = ack-nhfb,
  language =     "Chinese",
}

@Article{Parreira:2008:JAP,
  author =       "Josiane Xavier Parreira and Carlos Castillo and Debora
                 Donato and Sebastian Michel and Gerhard Weikum",
  title =        "The {Juxtaposed} approximate {PageRank} method for
                 robust {PageRank} approximation in a peer-to-peer web
                 search network",
  journal =      j-VLDB-J,
  volume =       "17",
  number =       "2",
  pages =        "291--313",
  month =        mar,
  year =         "2008",
  CODEN =        "VLDBFR",
  DOI =          "http://dx.doi.org/10.1007/s00778-007-0057-y",
  ISSN =         "1066-8888 (print), 0949-877X (electronic)",
  ISSN-L =       "1066-8888",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.math.utah.edu/pub/tex/bib/vldbj.bib",
  abstract =     "We present Juxtaposed approximate PageRank (JXP), a
                 distributed algorithm for computing PageRank-style
                 authority scores of Web pages on a peer-to-peer (P2P)
                 network. Unlike previous algorithms, JXP allows peers
                 to have overlapping content and requires no a priori
                 knowledge of other peers' content. Our algorithm
                 combines locally computed authority scores with
                 information obtained from other peers by means of
                 random meetings among the peers in the network. This
                 computation is based on a Markov-chain state-lumping
                 technique, and iteratively approximates global
                 authority scores. The algorithm scales with the number
                 of peers in the network and we show that the JXP scores
                 converge to the true PageRank scores that one would
                 obtain with a centralized algorithm. Finally, we show
                 how to deal with misbehaving peers by extending JXP
                 with a reputation model.",
  acknowledgement = ack-nhfb,
  fjournal =     "VLDB Journal: Very Large Data Bases",
  journal-URL =  "http://portal.acm.org/toc.cfm?id=J869",
  keywords =     "link analysis; Markov chain aggregation; peer-to-peer
                 systems; social reputation; Web graph",
}

@InProceedings{Pathak:2008:IDD,
  author =       "Amit Pathak and Soumen Chakrabarti and Manish Gupta",
  editor =       "{IEEE}",
  booktitle =    "{ICDE 2008: IEEE 24th International Conference on Data
                 Engineering}",
  title =        "Index Design for Dynamic Personalized {PageRank}",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "1489--1491",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/ICDE.2008.4497599",
  ISBN =         "1-4244-1836-4",
  ISBN-13 =      "978-1-4244-1836-7",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4497599",
  abstract =     "Personalized page rank, related to random walks with
                 restarts and conductance in resistive networks, is a
                 frequent search paradigm for graph-structured
                 databases. While efficient batch algorithms exist for
                 static whole-graph page rank, interactive query-time
                 personalized page rank has proved more challenging.
                 Here we describe how to select and build indices for a
                 popular class of page rank algorithms, so as to provide
                 real-time personalized page rank and smoothly trade off
                 between index size, preprocessing time, and query
                 speed. We achieve this by developing a precise, yet
                 efficiently estimated performance model for
                 personalized page rank query execution. We use this
                 model in conjunction with a query workload in a
                 cost-benefit type index optimizer. On millions of
                 queries from CiteSeer and its data graphs with 74--320
                 thousand nodes, our algorithm runs 50-400 $\times$
                 faster than whole-graph page rank, the gap growing with
                 graph size. Index size is 10--20\% of a text index.
                 Ranking accuracy is above 94\%.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4492792",
}

@InProceedings{Sarma:2008:EPG,
  author =       "Atish Das Sarma and Sreenivas Gollapudi and Rina
                 Panigrahy",
  title =        "Estimating {PageRank} on graph streams",
  crossref =     "Lenzerini:2008:PTS",
  pages =        "69--78",
  year =         "2008",
  DOI =          "http://doi.acm.org/10.1145/1376916.1376928",
  bibdate =      "Fri Jun 20 14:17:29 MDT 2008",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/pods.bib",
  abstract =     "This study focuses on computations on large graphs
                 (e.g., the web-graph) where the edges of the graph are
                 presented as a stream. The objective in the streaming
                 model is to use small amount of memory (preferably
                 sub-linear in the number of nodes $n$) and a few
                 passes.\par

                 In the streaming model, we show how to perform several
                 graph computations including estimating the probability
                 distribution after a random walk of length $l$, mixing
                 time, and the conductance. We estimate the mixing time
                 $M$ of a random walk in $\tilde{O}(n \alpha + M \alpha
                 \sqrt{n} +\sqrt{M n / \alpha})$ space and
                 $\tilde{O}(\sqrt{M} \alpha)$ passes. Furthermore, the
                 relation between mixing time and conductance gives us
                 an estimate for the conductance of the graph. By
                 applying our algorithm for computing probability
                 distribution on the Web-graph, we can estimate the
                 PageRank $p$ of any node up to an additive error of
                 $\sqrt{\epsilon} p$ in $\tilde{O}(\sqrt{M}/\alpha)$
                 passes and $\tilde{O}(\min(n \alpha + 1 / \epsilon
                 \sqrt{M} / \alpha + 1/\epsilon M \alpha, \alpha n
                 \sqrt{M} \alpha + 1/\epsilon \sqrt{M}/\alpha))$ space,
                 for any $\alpha \in (0, 1]$. In particular, for
                 $\epsilon = M / n$, by setting $\alpha = M^{-1/2}$, we
                 can compute the approximate PageRank values in
                 $\tilde{O}(n M^{-1/4})$ space and $\tilde{O}(M^{3/4})$
                 passes. In comparison, a standard implementation of the
                 PageRank algorithm will take $O(n)$ space and $O(M)$
                 passes.",
  acknowledgement = ack-nhfb,
  keywords =     "graph conductance; mixing time; PageRank; random walk;
                 streaming algorithms",
}

@Article{Sidi:2008:VEM,
  author =       "Avram Sidi",
  title =        "Vector extrapolation methods with applications to
                 solution of large systems of equations and to
                 {PageRank} computations",
  journal =      j-COMP-MATH-APPL,
  volume =       "56",
  number =       "1",
  pages =        "1--24",
  month =        jul,
  year =         "2008",
  CODEN =        "CMAPDK",
  DOI =          "http://dx.doi.org/10.1016/j.camwa.2007.11.027",
  ISSN =         "0898-1221 (print), 1873-7668 (electronic)",
  ISSN-L =       "0898-1221",
  MRclass =      "65F50 (65F10 65F15)",
  MRnumber =     "MR2427680 (2009j:65109)",
  MRreviewer =   "Cristina Tablino Possio",
  bibdate =      "Sat May 8 18:33:11 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1145.65312",
  abstract =     "An important problem that arises in different areas of
                 science and engineering is that of computing the limits
                 of sequences of vectors {x'n}, where x'n@?C^N with N
                 very large. Such sequences arise, for example, in the
                 solution of systems of linear or nonlinear equations by
                 fixed-point iterative methods, and lim'n'->'~x'n are
                 simply the required solutions. In most cases of
                 interest, however, these sequences converge to their
                 limits extremely slowly. One practical way to make the
                 sequences {x'n} converge more quickly is to apply to
                 them vector extrapolation methods. In this work, we
                 review two polynomial-type vector extrapolation methods
                 that have proved to be very efficient convergence
                 accelerators; namely, the minimal polynomial
                 extrapolation (MPE) and the reduced rank extrapolation
                 (RRE). We discuss the derivation of these methods,
                 describe the most accurate and stable algorithms for
                 their implementation along with the effective modes of
                 usage in solving systems of equations, nonlinear as
                 well as linear, and present their convergence and
                 stability theory. We also discuss their close
                 connection with the method of Arnoldi and with GMRES,
                 two well-known Krylov subspace methods for linear
                 systems. We show that they can be used very effectively
                 to obtain the dominant eigenvectors of large sparse
                 matrices when the corresponding eigenvalues are known,
                 and provide the relevant theory as well. One such
                 problem is that of computing the PageRank of the Google
                 matrix, which we discuss in detail. In addition, we
                 show that a recent extrapolation method of Kamvar et
                 al. that was proposed for computing the PageRank is
                 very closely related to MPE. We present a
                 generalization of the method of Kamvar et al. along
                 with a very economical algorithm for this
                 generalization. We also provide the missing convergence
                 theory for it.",
  acknowledgement = ack-nhfb,
  fjournal =     "Computers \& Mathematics with Applications. An
                 International Journal",
  keywords =     "Eigenvalue problems; Google matrix; Iterative methods;
                 Krylov subspace methods; Large sparse systems of
                 equations; Minimal polynomial extrapolation; PageRank
                 computations; Power iterations; Reduced rank
                 extrapolation; Singular linear systems; Stochastic
                 matrices; Vector extrapolation methods",
}

@InProceedings{Su:2008:ERR,
  author =       "Ja-Hwung Su and Bo-Wen Wang and Vincent S. Tseng",
  editor =       "{IEEE}",
  booktitle =    "{WI-IAT '08: IEEE\slash WIC\slash ACM International
                 Conference on Web Intelligence and Intelligent Agent
                 Technology (2008)}",
  title =        "Effective Ranking and Recommendation on {Web} Page
                 Retrieval by Integrating Association Mining and
                 {PageRank}",
  volume =       "3",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "455--458",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/WIIAT.2008.49",
  ISBN =         "0-7695-3496-1",
  ISBN-13 =      "978-0-7695-3496-1",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4740820",
  abstract =     "Nowadays, the well-known search engines, such as
                 Google, Yahoo, MSN, etc, have provided the users with
                 good search results based on special search strategies.
                 However there still exist some problems unsolved for
                 traditional search engines, including: (1) the gap
                 between user's intention and searched results is not
                 easy to narrow down under the global search space, and
                 (2) user's interested pages hidden in the local website
                 are not associated with the search results. To deal
                 with such problems, in this paper, we propose a novel
                 approach for personalized page ranking and
                 recommendation by integrating association mining and
                 PageRank so as to meet user's search goals. Moreover,
                 by mining the users' browsing behaviors, we can
                 successfully bridge the gap between global search
                 results and local preferences. The effectiveness of our
                 proposed approach was verified through experimental
                 evaluations.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4740404",
}

@InProceedings{Tripathy:2008:WMA,
  author =       "Animesh Tripathy and Prashanta K. Patra",
  editor =       "{IEEE}",
  booktitle =    "{APSCC '08: IEEE Asia-Pacific Services Computing
                 Conference (2008)}",
  title =        "A {Web} Mining Architectural Model of Distributed
                 Crawler for {Internet} Searches Using {PageRank}
                 Algorithm",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "513--518",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/APSCC.2008.259",
  ISBN =         "0-7695-3473-2",
  ISBN-13 =      "978-0-7695-3473-2",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4780726",
  abstract =     "As the World Wide Web is growing rapidly and data in
                 the present day scenario is stored in a distributed
                 manner. The need to develop a search engine based
                 architectural model for people to search through the
                 Web. Broad web search engines as well as many more
                 specialized search tools rely on web crawlers to
                 acquire large collections of pages for indexing and
                 analysis. The crawler is an important module of a web
                 search engine. The quality of a crawler directly
                 affects the searching quality of such web search
                 engines. Such a web crawler may interact with millions
                 of hosts over a period of weeks or months, and thus
                 issues of robustness, flexibility, and manageability
                 are of major importance. Given some URLs, the crawler
                 should retrieve the web pages of those URLs, parse the
                 HTML files, add new URLs into its queue and go back to
                 the first phase of this cycle. The crawler also can
                 retrieve some other information from the HTML files as
                 it is parsing them to get the new URLs. In this paper,
                 we describe the design of a web crawler that uses
                 PageRank algorithm for distributed searches and can be
                 run on a network of workstations. The crawler scales to
                 several hundred pages per second, is resilient against
                 system crashes and other events, and can be adapted to
                 various crawling applications. We present web mining
                 architecture of the system and describe efficient
                 techniques for achieving high performance.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4780614",
  keywords =     "Crawler; Data Mining; PageRank; Web Mining",
}

@MastersThesis{Tudisco:2008:MAN,
  author =       "F. Tudisco",
  title =        "Metodi analitico numerici per il problema del ranking
                 delle pagine web. (Italian) [{Numerical} analytic
                 method for the problem of ranking {Web} pages]",
  type =         "Bachelor thesis,",
  school =       "Dipartimento di Matematica, Universit{\`a} degli studi
                 di Roma ``Tor Vergata''",
  address =      "Rome, Italy",
  year =         "2008",
  bibdate =      "Wed Nov 30 08:15:21 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  language =     "Italian",
}

@Article{Wang:2008:DSZ,
  author =       "Xuanhui Wang and Tao Tao and Jian-Tao Sun and Azadeh
                 Shakery and Chengxiang Zhai",
  title =        "{DirichletRank}: {Solving} the zero-one gap problem of
                 {PageRank}",
  journal =      j-TOIS,
  volume =       "26",
  number =       "2",
  pages =        "10:1--10:??",
  month =        mar,
  year =         "2008",
  CODEN =        "ATISET",
  DOI =          "http://doi.acm.org/10.1145/1344411.1344416",
  ISSN =         "1046-8188",
  ISSN-L =       "0734-2047",
  bibdate =      "Thu Jun 12 16:52:34 MDT 2008",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tois/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/tois.bib",
  abstract =     "Link-based ranking algorithms are among the most
                 important techniques to improve web search. In
                 particular, the PageRank algorithm has been
                 successfully used in the Google search engine and has
                 been attracting much attention recently. However, we
                 find that PageRank has a ``zero-one gap'' problem
                 which, to the best of our knowledge, has not been
                 addressed in any previous work. This problem can be
                 potentially exploited to spam PageRank results and make
                 the state-of-the-art link-based antispamming techniques
                 ineffective. The zero-one gap problem arises as a
                 result of the current ad hoc way of computing
                 transition probabilities in the random surfing model.
                 We therefore propose a novel DirichletRank algorithm
                 which calculates these probabilities using Bayesian
                 estimation with a Dirichlet prior. DirichletRank is a
                 variant of PageRank, but does not have the problem of
                 zero-one gap and can be analytically shown
                 substantially more resistant to some link spams than
                 PageRank. Experiment results on TREC data show that
                 DirichletRank can achieve better retrieval accuracy
                 than PageRank due to its more reasonable allocation of
                 transition probabilities. More importantly, experiments
                 on the TREC dataset and another real web dataset from
                 the Webgraph project show that, compared with the
                 original PageRank, DirichletRank is more stable under
                 link perturbation and is significantly more robust
                 against both manually identified web spams and several
                 simulated link spams. DirichletRank can be computed as
                 efficiently as PageRank, and thus is scalable to
                 large-scale web applications.",
  acknowledgement = ack-nhfb,
  articleno =    "10",
  fjournal =     "ACM Transactions on Information Systems",
  keywords =     "DirichletRank; link analysis; PageRank; spamming;
                 zero-one gap",
}

@InProceedings{Wang:2008:KIS,
  author =       "Jinghua Wang and Jianyi Liu and Cong Wang and Ping
                 Zhang",
  booktitle =    "{ICNSC 2008: IEEE International Conference on
                 Networking, Sensing and Control}",
  title =        "Keyword Indexing System with {HowNet} and {PageRank}",
  crossref =     "IEEE:2008:PII",
  pages =        "389--393",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/ICNSC.2008.4525246",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4525246",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4489617",
}

@InProceedings{Wang:2008:RDR,
  author =       "Jue Wang and Jian Peng and Daping Zhang",
  editor =       "{IEEE}",
  booktitle =    "CSSE '08: Proceedings of the 2008 International
                 Conference on Computer Science and Software
                 Engineering",
  title =        "Research on Dynamic Reputation Management Model Based
                 on {PageRank}",
  volume =       "3",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "814--817",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/CSSE.2008.927",
  ISBN =         "0-7695-3336-1",
  ISBN-13 =      "978-0-7695-3336-0",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4722467",
  abstract =     "For the purpose of developing a usable trust
                 relationship between the resource providers (hosts) and
                 the resource consumers (users) in an open computing
                 environment and providing a unified management of the
                 reputation degree of the resource provides and users, a
                 dynamic reputation management model based on Google
                 PageRank (DRMPR) is proposed. The DRMPR system can
                 achieve self-study from a large amount of data and
                 feedback, and with the system obtaining a plenty of
                 resources, the judgment is more accurate. At the end of
                 the paper, an experimental project has been built to
                 demonstrate that the DRMPR can provide a unified
                 management of the reputation degree of the resource
                 provides and users accurately.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4721667",
  keywords =     "feedback; PageRank; reputation; trust",
}

@Article{Wu:2008:CJC,
  author =       "Gang Wu and Yimin Wei",
  title =        "Comments on: {``Jordan canonical form of the Google
                 matrix: a potential contribution to the PageRank
                 computation'' [SIAM J. Matrix Anal. Appl. {\bf 27}
                 (2005), no. 2, 305--312; MR2179674] by S.
                 Serra-Capizzano}",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "30",
  number =       "1",
  pages =        "364--374",
  year =         "2008",
  CODEN =        "SJMAEL",
  DOI =          "http://dx.doi.org/10.1137/070682204",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  MRclass =      "65F15 (15A57 65C40 65F10)",
  MRnumber =     "MR2399585 (2009c:65093)",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "See \cite{Serra-Capizzano:2005:JCF}.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
}

@Article{Wu:2008:EJC,
  author =       "Gang Wu",
  title =        "Eigenvalues and {Jordan} canonical form of a
                 successively rank-one updated complex matrix with
                 applications to {Google}'s {PageRank} problem",
  journal =      j-J-COMP-APPL-MATH,
  volume =       "216",
  number =       "2",
  pages =        "364--370",
  month =        jun,
  year =         "2008",
  CODEN =        "JCAMDI",
  DOI =          "http://dx.doi.org/10.1016/j.cam.2007.05.015",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  MRclass =      "15A18 (65F15 68U35); 15A21 65F15 15A18 15A57 68P10",
  MRnumber =     "MR2412913 (2009a:15037)",
  MRreviewer =   "Ross A. Lippert",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1148.15007",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
  keywords =     "65C40; 65F10; 65F15; Generalized Google matrix; Google
                 matrix; Jordan canonical form; Pagerank; Successively
                 rank-one updated matrix",
}

@InProceedings{Yang:2008:APT,
  author =       "Shenggang Yang and Jianmin Zhao and Xueyan Zhang and
                 Limei Zhao",
  editor =       "Elvis Wai Chung Leung and others",
  booktitle =    "{Advances in Blended Learning: Second Workshop on
                 Blended Learning, WBL 2008, Jinhua, China, August
                 20--22, 2008. Revised Selected Papers}",
  title =        "Application of {PageRank} Technique in Collaborative
                 Learning",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "102--109",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-89962-4_11",
  ISBN =         "3-540-89962-6",
  ISBN-13 =      "978-3-540-89962-4",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  abstract =     "With the rapid development in web 2.0, lots of realm
                 communities provide free platforms for users to enrich
                 their knowledge through online communication, sharing
                 and socializing without boundaries. As an on-line
                 system may interact with thousands of users, it is
                 almost impossible for the field experts or teachers to
                 give instant help manually, which is not only
                 inefficient, but also human laborious. To cope with it,
                 an E-learning community should construct an efficiency
                 knowledge acquiring mechanism. To assure this
                 mechanism, this research applies PageRank-based
                 mechanism to rank knowledge items synthetically. The
                 system appraises the knowledge items provided by
                 learners based on their rank, other users remarks and
                 most importantly teachers' and realm experts' remarks,
                 thus picks out the KIs to the knowledge base. In return
                 the users' grade will be upgraded or degraded by their
                 KIs. Learners are served with knowledge that best
                 matches their needs and encouraged by each other. Thus
                 this study sets up an aspiring and aggressive
                 collaborative learning environment. Experiments results
                 have shown that the developed system.",
  acknowledgement = ack-nhfb,
  keywords =     "Collaborative/cooperative learning; fairness gene;
                 knowledge acquiring; PageRank",
}

@InProceedings{Zhang:2008:NRA,
  author =       "Liyan Zhang and Chunping Li",
  editor =       "Wayne Wobcke and Mengjie Zhang",
  booktitle =    "Proceedings of the 21st Australasian Joint Conference
                 on Artificial Intelligence: Advances in Artificial
                 Intelligence",
  title =        "A Novel Recommending Algorithm Based on Topical
                 {PageRank}",
  volume =       "5360",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "447--453",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-89378-3_45",
  ISBN =         "3-540-89377-6",
  ISBN-13 =      "978-3-540-89377-6",
  LCCN =         "Q334 .A97 2008",
  bibdate =      "Sat May 8 18:33:07 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNAI,
  abstract =     "In this paper, we propose a Topical PageRank based
                 algorithm for recommender systems, which ranks products
                 by analyzing previous user-item relationships, and
                 recommends top-rank items to potentially interested
                 users. In order to rank all the items for each
                 particular user, we attempt to establish a correlation
                 graph among items, and implement ranking process with
                 our algorithm. We evaluate our algorithm on MovieLens
                 dataset and empirical experiments demonstrate that it
                 outperforms other state-of-the-art recommending
                 algorithms.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Zhang:2008:RAW,
  author =       "Yong Zhang and Long-bin Xiao and Bin Fan",
  booktitle =    "{FSKD '08: Fifth International Conference on Fuzzy
                 Systems and Knowledge Discovery (2008)}",
  title =        "The Research about {Web} Page Ranking Based on the
                 {A-PageRank} and the {Extended VSM}",
  crossref =     "Ma:2008:FFI",
  volume =       "4",
  pages =        "223--227",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1109/FSKD.2008.267",
  ISBN =         "0-7695-3305-1",
  ISBN-13 =      "978-0-7695-3305-6",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4666388",
  abstract =     "The web page rank algorithm is always regarded as the
                 core of the search engine. Firstly, this article
                 analyzes the traditional and classical rank algorithms
                 briefly. Then, it proposes a new rank algorithm, which
                 is called A-PageRank. In this algorithm, the PageRank
                 value of the source page is distributed to its Link-out
                 pages according to the topic similarity. Lastly, a new
                 method which uses both the similarity and divergence to
                 weigh the match degree between one web page and one
                 user query is adopted in order to increase the
                 precision and recall rate of the search engine.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4665920",
  keywords =     "A-PageRank; anchor text; PageRank; PFT; VSM",
}

@InProceedings{Zhang:2008:TPB,
  author =       "Liyan Zhang and Kai Zhang and Chunping Li",
  editor =       "{ACM}",
  booktitle =    "Annual ACM Conference on Research and Development in
                 Information Retrieval Proceedings of the 31st annual
                 international ACM SIGIR conference on Research and
                 development in information retrieval",
  title =        "A topical {PageRank} based algorithm for recommender
                 systems",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "713--714",
  year =         "2008",
  DOI =          "http://dx.doi.org/10.1145/1148170.1148189",
  ISBN =         "1-60558-164-X",
  ISBN-13 =      "978-1-60558-164-4",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In this paper, we propose a Topical PageRank based
                 algorithm for recommender systems, which aim to rank
                 products by analyzing previous user-item relationships,
                 and recommend top-rank items to potentially interested
                 users. We evaluate our algorithm on MovieLens dataset
                 and empirical experiments demonstrate that it
                 outperforms other state-of-the-art recommending
                 algorithms.",
  acknowledgement = ack-nhfb,
  keywords =     "recommender system; topical PageRank",
}

@InProceedings{Agirre:2009:PPW,
  author =       "Eneko Agirre and Aitor Soroa",
  editor =       "Alex Lascarides and Claire Gardent and Joakim Nivre",
  booktitle =    "{Proceedings of the 12th Conference of the European
                 Chapter of the Association for Computational
                 Linguistics: 30 March--3 April 2009, Megaron Athens
                 International Conference Centre, Athens, Greece}",
  title =        "Personalizing {PageRank} for word sense
                 disambiguation",
  publisher =    "Association for Computational Linguistics",
  address =      "Morristown, NJ, USA",
  pages =        "33--41",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/ICSC.2007.107",
  ISBN =         "1-932432-16-7",
  ISBN-13 =      "978-1-932432-16-9",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In this paper we propose a new graph-based method that
                 uses the knowledge in a LKB (based on WordNet) in order
                 to perform unsupervised Word Sense Disambiguation. Our
                 algorithm uses the full graph of the LKB efficiently,
                 performing better than previous approaches in English
                 all-words datasets. We also show that the algorithm can
                 be easily ported to other languages with good results,
                 with the only requirement of having a WordNet. In
                 addition, we make an analysis of the performance of the
                 algorithm, showing that it is efficient and that it
                 could be tuned to be faster.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Alam:2009:FPC,
  author =       "Md. Hijbul Alam and Jongwoo Ha and Sangkeun Lee",
  editor =       "Xiaofang Zhou and others",
  booktitle =    "Proceedings of the 14th International Conference on
                 Database Systems for Advanced Applications",
  title =        "Fractional {PageRank} Crawler: Prioritizing {URLs}
                 Efficiently for Crawling Important Pages Early",
  volume =       "5463",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "590--594",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1007/978-3-642-00887-0_52",
  ISBN =         "3-642-00886-0",
  ISBN-13 =      "978-3-642-00886-3",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA76.9.D3 I58 2009",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  abstract =     "Crawling important pages early is a well studied
                 problem. However, the availability of different types
                 of framework for publishing web content greatly
                 increases the number of web pages. Therefore, the
                 crawler should be fast enough to prioritize and
                 download the important pages. As the importance of a
                 page is not known before or during its download, the
                 crawler needs a great deal of time to approximate the
                 importance to prioritize the download of the web pages.
                 In this research, we propose Fractional PageRank
                 crawlers that prioritize the downloaded pages for the
                 purpose of discovering important URLs early during the
                 crawl. Our experiments demonstrate that they improve
                 the running time dramatically while crawling the
                 important pages early.",
  acknowledgement = ack-nhfb,
  bookpages =    "xix + 797",
}

@Article{Bar-Yossef:2009:DCD,
  author =       "Ziv Bar-Yossef and Idit Keidar and Uri Schonfeld",
  title =        "Do not crawl in the {DUST}: {Different URLs with
                 Similar Text}",
  journal =      j-TWEB,
  volume =       "3",
  number =       "1",
  pages =        "3:1--3:??",
  month =        jan,
  year =         "2009",
  CODEN =        "????",
  DOI =          "http://doi.acm.org/10.1145/1462148.1462151",
  ISSN =         "1559-1131 (print), 1559-114X (electronic)",
  ISSN-L =       "1559-1131",
  bibdate =      "Fri Apr 24 18:18:15 MDT 2009",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/tweb.bib",
  abstract =     "We consider the problem of DUST: Different URLs with
                 Similar Text. Such duplicate URLs are prevalent in Web
                 sites, as Web server software often uses aliases and
                 redirections, and dynamically generates the same page
                 from various different URL requests. We present a novel
                 algorithm, {\em DustBuster}, for uncovering DUST; that
                 is, for discovering rules that transform a given URL to
                 others that are likely to have similar content.
                 DustBuster mines DUST effectively from previous crawl
                 logs or Web server logs, {\em without\/} examining page
                 contents. Verifying these rules via sampling requires
                 fetching few actual Web pages. Search engines can
                 benefit from information about DUST to increase the
                 effectiveness of crawling, reduce indexing overhead,
                 and improve the quality of popularity statistics such
                 as PageRank.",
  acknowledgement = ack-nhfb,
  articleno =    "3",
  fjournal =     "ACM Transactions on the Web (TWEB)",
  keywords =     "antialiasing; crawling; duplicate detection; Search
                 engines; URL normalization",
}

@Article{Boldi:2009:PFD,
  author =       "Paolo Boldi and Massimo Santini and Sebastiano
                 Vigna",
  title =        "{PageRank}: {Functional} dependencies",
  journal =      j-TOIS,
  volume =       "27",
  number =       "4",
  pages =        "19:1--19:??",
  month =        nov,
  year =         "2009",
  CODEN =        "ATISET",
  DOI =          "http://dx.doi.org/10.1145/1062745.1062826",
  ISSN =         "1046-8188",
  ISSN-L =       "0734-2047",
  bibdate =      "Mon Mar 15 12:37:02 MDT 2010",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tois/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  articleno =    "19",
  fjournal =     "ACM Transactions on Information Systems",
  keywords =     "damping factor; PageRank; power method",
}

@InProceedings{Chen:2009:IPA,
  author =       "Xiaoyun Chen and Baojun Gao and Ping Wen",
  editor =       "Xin Li and Wenbin Hu and others",
  booktitle =    "{Proceedings, 2009 International Conference on
                 Information Engineering and Computer Science: ICIECS
                 2009, Wuhan China 19--20 December 2009}",
  title =        "An Improved {PageRank} Algorithm Based on Latent
                 Semantic Model",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "1--4",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/ICIECS.2009.5364637",
  ISBN =         "1-4244-4994-4",
  ISBN-13 =      "978-1-4244-4994-1",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "IEEE catalog number CFP0990H.",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5364637",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5362513",
}

@InProceedings{Chen:2009:SNE,
  author =       "Wei Chen and Shang-Hua Teng and Yajun Wang and Yuan
                 Zhou",
  title =        "On the $\alpha$-sensitivity of {Nash} equilibria in
                 {PageRank}-based network reputation games",
  crossref =     "Deng:2009:FAT",
  volume =       "5598",
  pages =        "63--73",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1007/978-3-642-02270-8_9",
  ISBN =         "3-642-02269-3",
  ISBN-13 =      "978-3-642-02269-2",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "????",
  MRclass =      "68Wxx",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  ZMnumber =     "05578464",
  abstract =     "Web search engines use link-based reputation systems
                 (e.g. PageRank) to measure the importance of web pages,
                 giving rise to the strategic manipulations of
                 hyperlinks by spammers and others to boost their web
                 pages' reputation scores. Hopcroft and Sheldon [10]
                 study this phenomenon by proposing a network formation
                 game in which nodes strategically select their outgoing
                 links in order to maximize their PageRank scores. They
                 pose an open question in [10] asking whether all Nash
                 equilibria in the PageRank game are insensitive to the
                 restart probability $\alpha$ of the PageRank algorithm.
                 They show that a positive answer to the question would
                 imply that all Nash equilibria in the PageRank game
                 must satisfy some strong algebraic symmetry, a property
                 rarely satisfied by real web graphs. In this paper, we
                 give a negative answer to this open question. We
                 present a family of graphs that are Nash equilibria in
                 the PageRank game only for certain choices of
                 $\alpha$.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Chung:2009:LGP,
  author =       "Fan Chung",
  title =        "A Local Graph Partitioning Algorithm Using Heat Kernel
                 {PageRank}",
  crossref =     "Avrachenkov:2009:AMW",
  pages =        "62--75",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-95995-3_6",
  ISBN =         "3-540-95994-7",
  ISBN-13 =      "978-3-540-95994-6",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "????",
  MRclass =      "68M10",
  bibdate =      "Sat May 8 18:33:04 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  ZMnumber =     "05505865",
  abstract =     "We give an improved local partitioning algorithm using
                 heat kernel pagerank, a modified version of PageRank.
                 For a subset S with Cheeger ratio (or conductance) h,
                 we show that there are at least a quarter of the
                 vertices in S that can serve as seeds for heat kernel
                 pagerank which lead to local cuts with Cheeger ratio at
                 most $O(\sqrt{h})$, improving the previously bound by a
                 factor of $\sqrt{log|S|}$.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Deng:2009:GEF,
  author =       "Kaiying Deng and Tieli Sun and Jingwei Deng",
  editor =       "{IEEE}",
  booktitle =    "{FSKD '09: Sixth International Conference on Fuzzy
                 Systems and Knowledge Discovery (2009)}",
  title =        "The General Extrapolation Formula for Acceleration
                 {PageRank} Computations",
  volume =       "7",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "590--594",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/FSKD.2009.112",
  ISBN =         "0-7695-3735-9",
  ISBN-13 =      "978-0-7695-3735-1",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5360078",
  abstract =     "Based on the foundation work for PageRank
                 computations, we further derive the general formula for
                 accelerating PageRank computations. And we also discuss
                 the method for generating high dimension stochastic
                 matrix, being characterized the Web graph. Numerical
                 results confirm the effectiveness of the theoretical
                 analysis and numerical algorithms.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5358480",
  keywords =     "Hyperlink Analysis; Information Retrieval; PageRank",
}

@Article{Ding:2009:PRA,
  author =       "Ying Ding and Erjia Yan and Arthur Frazho and James
                 Caverlee",
  title =        "{PageRank} for ranking authors in co-citation
                 networks",
  journal =      "Journal of the American Society for Information
                 Science and Technology",
  volume =       "60",
  number =       "11",
  pages =        "2229--2243",
  month =        nov,
  year =         "2009",
  CODEN =        "JASIEF",
  DOI =          "http://dx.doi.org/10.1145/1013367.1013519",
  ISSN =         "1532-2882 (print), 1532-2890 (electronic)",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "This paper studies how varied damping factors in the
                 PageRank algorithm influence the ranking of authors and
                 proposes weighted PageRank algorithms. We selected the
                 108 most highly cited authors in the information
                 retrieval (IR) area from the 1970s to 2008 to form the
                 author co-citation network. We calculated the ranks of
                 these 108 authors based on PageRank with the damping
                 factor ranging from 0.05 to 0.95. In order to test the
                 relationship between different measures, we compared
                 PageRank and weighted PageRank results with the
                 citation ranking, h-index, and centrality measures. We
                 found that in our author co-citation network, citation
                 rank is highly correlated with PageRank with different
                 damping factors and also with different weighted
                 PageRank algorithms; citation rank and PageRank are not
                 significantly correlated with centrality measures; and
                 h-index rank does not significantly correlate with
                 centrality measures but does significantly correlate
                 with other measures. The key factors that have impact
                 on the PageRank of authors in the author co-citation
                 network are being co-cited with important authors.",
  acknowledgement = ack-nhfb,
  ajournal =     "J. Am. Soc. Inf. Sci. Technol.",
  fjournal =     "Journal of the American Society for Information
                 Science and Technology",
  keywords =     "authors; citation analysis; co-citation networks;
                 ranking; weighting",
}

@InProceedings{Gao:2009:KNM,
  author =       "Lianxiong Gao and Jianping Wu and Liu Rui",
  booktitle =    "{CCDC '09: Chinese Control and Decision Conference
                 (2009)}",
  title =        "Key nodes mining in transport networks based in
                 {PageRank} algorithm",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "4413--4416",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/CCDC.2009.5192339",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5192339",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5174536",
}

@InProceedings{Imran:2009:ERP,
  author =       "Naveed Imran and Jingen Liu and Jiebo Luo and Mubarak
                 Shah",
  editor =       "{ACM}",
  booktitle =    "International Multimedia Conference Proceedings of the
                 seventeen ACM international conference on Multimedia",
  title =        "Event recognition from photo collections via
                 PageRank",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "621--624",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/ICCV.2005.20",
  ISBN =         "1-60558-608-0",
  ISBN-13 =      "978-1-60558-608-3",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:08 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We propose a method of mining most informative
                 features for the event recognition from photo
                 collections. Our goal is to classify different event
                 categories based on the visual content of a group of
                 photos that constitute the event. Such photo groups are
                 typical in a personal photo collection of different
                 events. Visual features are extracted from the images,
                 yet the features from individual images are often noisy
                 and not all of them represent the distinguishing
                 characteristics of an event. We employ the PageRank
                 technique to mine the most informative features from
                 the images that belong to the same event. Subsequently,
                 we classify different event categories using the
                 multiple images of the same event because we argue that
                 they are more informative about the content of an event
                 rather than any single image. We compare our proposed
                 approach with the standard bag of features method (BOF)
                 and observe considerable improvements in recognition
                 accuracy.",
  acknowledgement = ack-nhfb,
  keywords =     "CBIR; event category recognition; pagerank",
}

@InProceedings{Ishii:2009:DPC,
  author =       "Hideaki Ishii and Roberto Tempo",
  editor =       "{IEEE}",
  booktitle =    "{ACC '09: American Control Conference (2009)}",
  title =        "Distributed {PageRank} computation with link
                 failures",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "1976--1981",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/ACC.2009.5160351",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5160351",
  abstract =     "The Google search engine employs the so-called
                 PageRank algorithm for ranking the search results. This
                 algorithm quantifies the importance of each web page
                 based on the link structure of the web. In this paper,
                 we continue our recent work on distributed randomized
                 computation of PageRank, where the pages locally
                 determine their values by communicating with linked
                 pages. In particular, we propose a distributed
                 randomized algorithm with limited information, where
                 only part of the linked pages is required to be
                 contacted. This is useful to enhance flexibility and
                 robustness in computation and communication.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5089257",
  keywords =     "distributed computation; link failures; multi-agent
                 consensus; pagerank algorithm; randomization;
                 stochastic matrices",
}

@InProceedings{Ishii:2009:DRP,
  author =       "H. Ishii and R. Tempo and Er-Wei Bai and F. Dabbene",
  booktitle =    "{CDC\slash CCC 2009: Proceedings of the 48th IEEE
                 Conference on Decision and Control [held jointly with
                 the 2009 28th Chinese Control Conference]}",
  title =        "Distributed randomized {PageRank} computation based on
                 web aggregation",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "3026--3031",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/CDC.2009.5399514",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5399514",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379695",
}

@InProceedings{Ishii:2009:FLS,
  author =       "H. Ishii and R. Tempo",
  booktitle =    "{CDC\slash CCC 2009: Proceedings of the 48th IEEE
                 Conference on Decision and Control [held jointly with
                 the 2009 28th Chinese Control Conference]}",
  title =        "Fragile link structure in {PageRank} computation",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "121--126",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/CDC.2009.5399501",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5399501",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379695",
}

@InProceedings{Jager:2009:PSH,
  author =       "Douglas V. Jager and Jeremy T. Bradley",
  editor =       "Leif Azzopardi and others",
  booktitle =    "{Advances in information retrieval theory: second
                 International Conference on the Theory of Information
                 Retrieval, ICTIR 2009, Cambridge, UK, September 10--12,
                 2009: proceedings}",
  title =        "{PageRank}: Splitting Homogeneous Singular Linear
                 Systems of Index One",
  volume =       "5766",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "17--28",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1007/978-3-642-04417-5_3",
  ISBN =         "3-642-04416-6",
  ISBN-13 =      "978-3-642-04416-8",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA76.9.D3 I55887 2009",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  abstract =     "The PageRank algorithm is used today within web
                 information retrieval to provide a content-neutral
                 ranking metric over web pages. It employs power method
                 iterations to solve for the steady-state vector of a
                 DTMC. The defining one-step probability transition
                 matrix of this DTMC is derived from the hyperlink
                 structure of the web and a model of web surfing
                 behaviour which accounts for user bookmarks and
                 memorised URLs. \par

                 In this paper we look to provide a more accessible,
                 more broadly applicable explanation than has been given
                 in the literature of how to make PageRank calculation
                 more tractable through removal of the dangling-page
                 matrix. This allows web pages without outgoing links to
                 be removed before we employ power method iterations. It
                 also allows decomposition of the problem according to
                 irreducible subcomponents of the original transition
                 matrix. Our explanation also covers a PageRank
                 extension to accommodate TrustRank. In setting out our
                 alternative explanation, we introduce and apply a
                 general linear algebraic theorem which allows us to map
                 homogeneous singular linear systems of index one to
                 inhomogeneous non-singular linear systems with a shared
                 solution vector. As an aside, we show in this paper
                 that irreducibility is not required for PageRank to be
                 well-defined.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Jin:2009:APA,
  author =       "Ying Jin and Jing Zhang and Pengfei Ma and Weiping Hao
                 and Shutong Luo and Zepeng Li",
  editor =       "{IEEE}",
  booktitle =    "{COMPSAC '09: 33rd Annual IEEE International Computer
                 Software and Applications Conference, 2009}",
  title =        "Applying {PageRank} Algorithm in Requirement Concern
                 Impact Analysis",
  volume =       "1",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "361--366",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/COMPSAC.2009.55",
  ISBN =         "0-7695-3726-X",
  ISBN-13 =      "978-0-7695-3726-9",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5254238",
  abstract =     "As an important part of requirement management,
                 managing requirement change plays a key role in
                 controlling project schedule and costs at early stage.
                 Effective requirement impact analysis would give proper
                 assessment on the effect of certain requirement changes
                 on the whole system, and provide useful information for
                 making trade-off decisions on future system design and
                 implementation. In this paper a quantitative approach
                 to concern impact analysis at requirement level has
                 been proposed with the application of PageRank
                 algorithm, which is a successful link based web page
                 sorting algorithm. At first, separation of concerns is
                 applied during deriving formal requirement
                 specification from textual requirement statements.
                 Next, concerns are specified and concern relationship
                 graph is established. Finally, PageRank algorithm is
                 utilized on concern relationship graph for assessing
                 the impact of concern changes. Our approach has been
                 applied to hallway section in Light Control System and
                 validation of analysis result has been stated.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5254044",
  keywords =     "concern impact analysis; concern relationship graph;
                 PageRank algorithm",
}

@Article{Kaul:2009:RBW,
  author =       "Rohit Kaul and Yeogirl Yun and Seong-Gon Kim",
  title =        "Ranking billions of {Web} pages using diodes",
  journal =      j-CACM,
  volume =       "52",
  number =       "8",
  pages =        "132--136",
  month =        aug,
  year =         "2009",
  CODEN =        "CACMA2",
  DOI =          "http://doi.acm.org/10.1145/1536616.1536649",
  ISSN =         "0001-0782 (print), 1557-7317 (electronic)",
  ISSN-L =       "0001-0782",
  bibdate =      "Wed Sep 2 16:54:35 MDT 2009",
  bibsource =    "http://www.acm.org/pubs/contents/journals/cacm/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.math.utah.edu/pub/tex/bib/cacm2000.bib",
  abstract =     "Introduction\par

                 Because of the web's rapid growth and lack of central
                 organization, Internet search engines play a vital role
                 in assisting the users of the Web in retrieving
                 relevant information out of the tens of billions of
                 documents available. With millions of dollars of
                 potential revenue at stake, commercial Web sites
                 compete fiercely to be placed prominently within the
                 first page returned by a search engine. As a result,
                 search engine optimizers (SEOs) developed various forms
                 of search engine spamming (or spamdexing) techniques to
                 artificially inflate the rankings of Web pages.
                 Link-based ranking algorithms, such as Google's
                 PageRank, have been largely effective against most
                 conventional spamming techniques.\par

                 However, PageRank has three fundamental flaws that,
                 when exploited aggressively, can be proven to be its
                 Achilles' heel: First, PageRank gives a minimum
                 guaranteed score to every page on the Web; second, it
                 rewards all incoming links as valid endorsements; and
                 third, it imposes no penalty for making links to
                 low-quality pages. SEOs can take advantage of these
                 shortcomings to the extreme by employing an Artificial
                 Web, a collection of an extremely large number of
                 computer-generated Web pages containing many links to
                 only a few target pages. Each page of the Artificial
                 Web collects the minimum PageRank and feeds it back to
                 the target pages. Although the individual endorsements
                 are small, the flaws of PageRank make it possible for
                 an Artificial Web to accumulate sizable PageRank values
                 for the target pages. The SEOs can even download a
                 substantial portion of the real Web and modify only the
                 destinations of the hyperlinks, thus circumventing any
                 detection algorithms based on the quality or the size
                 of pages. As the size of an Artificial Web can be
                 comparable to that of the real Web, SEOs can seriously
                 compromise the objectivity of the results that PageRank
                 provides. Although some statistical measures can be
                 employed to identify specific attributes associated
                 with an Artificial Web and filter them out of search
                 results, it is far more desirable to develop a new
                 ranking model that is free of such exploits to begin
                 with.",
  acknowledgement = ack-nhfb,
  fjournal =     "Communications of the ACM",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J79",
}

@InProceedings{Lianxiong:2009:KNM,
  author =       "Gao Lianxiong and Wu Jianping and Liu Rui",
  editor =       "{IEEE}",
  booktitle =    "Proceedings of the 21st annual international
                 conference on Chinese control and decision conference",
  title =        "Key nodes mining in transport networks based on
                 {PageRank} algorithm",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "4449--4452",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1137/S0036144503424786",
  ISBN =         "1-4244-2722-3",
  ISBN-13 =      "978-1-4244-2722-2",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Transport networks display the features of complex
                 networks, in which the vertices importance measurement
                 is crucial. After analyzing some classic importance
                 measurements and the characteristics of transport
                 networks, NodeRank, a new method based on PageRank
                 algorithm, is proposed in this paper to measure the
                 importance of vertices in transportation network. Then
                 the constraint equation is deduced and the existence
                 and uniqueness of solutions are presented. The solving
                 algorithm is described and its convergence is analyzed.
                 Finally, we present a case applying our method to
                 mining key nodes in a real-world transport network.",
  acknowledgement = ack-nhfb,
  keywords =     "complex network; key nodes mining; pagerank algorithm;
                 transport network",
}

@Article{Lin:2009:CPL,
  author =       "Yiqin Lin and Xinghua Shi and Yimin Wei",
  title =        "On computing {PageRank} via lumping the {Google}
                 matrix",
  journal =      j-J-COMP-APPL-MATH,
  volume =       "224",
  number =       "2",
  pages =        "702--708",
  month =        feb,
  year =         "2009",
  CODEN =        "JCAMDI",
  DOI =          "http://dx.doi.org/10.1016/j.cam.2008.06.003",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  MRclass =      "65F15",
  MRnumber =     "MR2492903 (2009k:65071)",
  MRreviewer =   "David Scott Watkins",
  bibdate =      "Sat May 8 18:33:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Computing Google's PageRank via lumping the Google
                 matrix was recently analyzed in [I. C. F. Ipsen, T. M.
                 Selee, PageRank computation, with special attention to
                 dangling nodes, SIAM J. Matrix Anal. Appl. 29 (2007)
                 1281--1296]. It was shown that all of the dangling
                 nodes can be lumped into a single node and the PageRank
                 could be obtained by applying the power method to the
                 reduced matrix. Furthermore, the stochastic reduced
                 matrix had the same nonzero eigenvalues as the full
                 Google matrix and the power method applied to the
                 reduced matrix had the same convergence rate as that of
                 the power method applied to the full matrix. Therefore,
                 a large amount of operations could be saved for
                 computing the full PageRank vector. In this note, we
                 show that the reduced matrix obtained by lumping the
                 dangling nodes can be further reduced by lumping a
                 class of nondangling nodes, called weakly nondangling
                 nodes, to another single node, and the further reduced
                 matrix is also stochastic with the same nonzero
                 eigenvalues as the Google matrix.",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
  keywords =     "65B99; 65F10; 65F15; 65F50; Dangling node; Google
                 matrix; Lumping; PageRank; Power method; Weakly
                 nondangling node",
}

@InProceedings{Ling:2009:IPW,
  author =       "Zhang Ling and Qin Zheng",
  editor =       "{IEEE}",
  booktitle =    "ICISE Proceedings of the 2009 First IEEE International
                 Conference on Information Science and Engineering",
  title =        "The Improved {PageRank} in {Web} Crawler",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "1889--1892",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/ICISE.2009.1220",
  ISBN =         "0-7695-3887-8",
  ISBN-13 =      "978-0-7695-3887-7",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:04 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Pagerank is an algorithm for rating web pages. It
                 introduces the relationship of citation in academic
                 papers to evaluate the web page's authority. It gives
                 the same weight to all edges and ignores the relevancy
                 of web pages to the topic, resulting in a problem of
                 topic-drift. On the analysis of several pagerank
                 algorithms, an improved pagerank based upon thematic
                 segments is proposed. In this algorithm, a web page is
                 divided into several blocks by Html document's
                 structure and the most weight is given to linkages in
                 the block that is most relevant to given topic.
                 Moreover, the visited outlinks are regarded as feedback
                 to modify blocks' relevancy The experiment on Web
                 crawler shows that the new algorithm has some effect on
                 resolving the problem of topic-drift.",
  acknowledgement = ack-nhfb,
}

@InProceedings{Litvak:2009:CTD,
  author =       "Nelly Litvak and Werner Scheinhardt and Yana Volkovich
                 and Bert Zwart",
  title =        "Characterization of Tail Dependence for In-Degree and
                 {PageRank}",
  crossref =     "Avrachenkov:2009:AMW",
  pages =        "90--103",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1007/978-3-540-95995-3_8",
  ISBN =         "3-540-95994-7",
  ISBN-13 =      "978-3-540-95994-6",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "????",
  bibdate =      "Sat May 8 18:33:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  abstract =     "The dependencies between power law parameters such as
                 in-degree and PageRank, can be characterized by the
                 so-called angular measure, a notion used in extreme
                 value theory to describe the dependency between very
                 large values of coordinates of a random vector. Basing
                 on an analytical stochastic model, we argue that the
                 angular measure for in-degree and personalized PageRank
                 is concentrated in two points. This corresponds to the
                 two main factors for high ranking: large in-degree and
                 a high rank of one of the ancestors. Furthermore, we
                 can formally establish the relative importance of these
                 two factors.",
  acknowledgement = ack-nhfb,
  keywords =     "Multivariate extremes; PageRank; Power law graphs;
                 Regular variation",
}

@InProceedings{Liu:2009:ERE,
  author =       "Yaqing Liu and Rong Chen and Hong Yang",
  booktitle =    "{ICIECS 2009: International Conference on Information
                 Engineering and Computer Science}",
  title =        "Entity-Relation Extraction for {Chinese} Based on
                 Pattern Evolution and {PageRank}",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "1--4",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/ICIECS.2009.5364487",
  ISBN =         "1-4244-4994-4",
  ISBN-13 =      "978-1-4244-4994-1",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5364487",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5362513",
}

@Book{Lowe:2009:GSS,
  author =       "Janet Lowe",
  title =        "{Google} speaks: secrets of the world's greatest
                 billionaire entrepreneurs, {Sergey Brin} and {Larry
                 Page}",
  publisher =    pub-WILEY,
  address =      pub-WILEY:adr,
  pages =        "xiii + 315",
  year =         "2009",
  ISBN =         "0-470-50122-7 (e-book), 0-470-50124-3 (e-book: Adobe
                 Digital Editions), 0-470-50123-5 (e-book: Mobipocket
                 Reader), 0-470-39854-X (cloth)",
  ISBN-13 =      "978-0-470-50122-1 (e-book), 978-0-470-50124-5 (e-book:
                 Adobe Digital Editions), 978-0-470-50123-8 (e-book:
                 Mobipocket Reader), 978-0-470-39854-8 (cloth)",
  LCCN =         "QA76.2.A2 L69 2009eb",
  bibdate =      "Fri Jun 3 09:52:48 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  abstract =     "An up-close look at the people and philosophies behind
                 one of the most important new companies of our time,
                 Google Speaks is an engaging and informative look at
                 one of the most important companies of the twenty-first
                 century. It reveals the amazing story behind Google, a
                 company that in less than 15 years has become a global
                 household name and, in the process, created a new model
                 for corporate responsibility and employee relations.
                 Lowe explores the values that drive Google's founders
                 and discusses how they have created a culture that
                 fosters creativity and fun, while at the same time,
                 keeping Google at the forefront of technology through
                 large, relentless R and D investments and imaginative
                 partnerships with organizations such as NASA. This book
                 also addresses controversies surrounding Google, such
                 as copyright infringement, antitrust concerns, and
                 personal privacy.",
  acknowledgement = ack-nhfb,
  subject =      "Brin, Sergey; Page, Larry; Computer programmers;
                 United States; Biography; Businesspeople; Internet
                 programming; Google; Web search engines",
  subject-dates = "1973--; 1973--",
  tableofcontents = "Introduction \\
                 The Google guys: Sergey Brin; Larry Page; The power of
                 partnership; Networking at its best; Burning man \\
                 Adult supervision: The collective wisdom of Silicon
                 Valley; He's been the rock: they've been the rockets; A
                 man of influence; Climbing a different kind of mountain
                 \\
                 In the beginning: The ultimate search engine; Not
                 inventing, but improving upon; Look around you for
                 inspiration; How search works; Platform power; Open
                 platform \\
                 Google by any other name: A blessed blunder; From noun
                 to verb; Playing with the name; The Google logo; The
                 Google doodle; Google zeitgeist \\
                 A company is born: Yahoo! drew the map; The requisite
                 garage; The venture capitalists; The elusive business
                 plan; Investing in wild ideas; Good ideas put to good
                 use; Dealing with dark matter; Aversion to advertising;
                 Advertising that delivers results; Two ways to
                 advertise: AdWords and AdSense; Extending the Google
                 reach; The science of advertising; Google didn't
                 advertise itself - at first; Birth of the Google
                 economy \\
                 Going public: ``We're different''; The Dutch auction;
                 The Playboy interview; Ten years later \\
                 The vision: Make it useful; Make it big; Make it fun;
                 Don't do evil; Make it free \\
                 Google culture: New management style; Ten things Google
                 has found to be true; Riding the long tail; 20 percent
                 projects; Perpetual beta; Fabled workplace; An
                 alternative point of view; Googleplex; Google in
                 Ireland; Top ten reasons to work at Google; The battle
                 for brainpower; Guarding the secrets \\
                 Google grows up: Conflicts and controversy: Click
                 fraud; Avoiding - or not avoiding - pornography;
                 Privacy issue; Advertising products; Gmail; Street
                 view; Can they snoop - and will they tell?; Hello,
                 human rights; The great Chinese firewall; Principles of
                 freedom; Copyright infringement; The authors' revolt;
                 The game-changing settlement; Lawsuits everywhere;
                 Google gets an airplane; Google gets a satellite \\
                 Good citizen Google: Google.org: the philanthropic
                 part; Google and the environment; Renewable energy less
                 than coal; Geothermal power; Energy from the sea;
                 Energy-efficient Googleplex \\
                 Google's future: Artificial intelligence; Onward to Web
                 3.0; Cloud computing; YouTube; The Google phone; White
                 spaces \\
                 The dominant power in the industry?: Google Microsoft,
                 and the Internet civil war; The battle of Yahoo!; Gates
                 on Google \\
                 Conclusion: Lessons from Larry and Sergey; The traits
                 of those who change the world \\
                 Timeline \\
                 Glossary",
}

@InProceedings{Mataoui:2009:EPA,
  author =       "M. Mataoui and M. Boughanem and M. Mezghiche",
  booktitle =    "{ICADIWT '09: Second International Conference on the
                 Applications of Digital Information and Web
                 Technologies (2009)}",
  title =        "Experiments on {PageRank} algorithm in the {XML}
                 information retrieval context",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "393--398",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/ICADIWT.2009.5273944",
  ISBN =         "1-4244-4456-X",
  ISBN-13 =      "978-1-4244-4456-4",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5273944",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5254891",
}

@InProceedings{Nazin:2009:ARA,
  author =       "A. Nazin and B. Polyak",
  booktitle =    "{CDC\slash CCC 2009: Proceedings of the 48th IEEE
                 Conference on Decision and Control [held jointly with
                 the 2009 28th Chinese Control Conference]}",
  title =        "Adaptive randomized algorithm for finding eigenvector
                 of stochastic matrix with application to {PageRank}",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "127--132",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/CDC.2009.5400036",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5400036",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5379695",
}

@InProceedings{Nazin:2009:RAFa,
  author =       "A. Nazin and B. Polyak",
  booktitle =    "{ISIC 2009: IEEE Control Applications, (CCA) \&
                 Intelligent Control}",
  title =        "A randomized algorithm for finding eigenvector of
                 stochastic matrix with application to {PageRank}
                 problem",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "412--416",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/CCA.2009.5280707",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5280707",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5268173",
}

@Article{Nazin:2009:RAFb,
  author =       "A. V. Nazin and B. T. Polyak",
  title =        "A randomized algorithm for finding an eigenvector of a
                 stochastic matrix with application to {PageRank}",
  journal =      j-DOKL-AKAD-NAUK,
  volume =       "426",
  number =       "6",
  pages =        "734--737",
  year =         "2009",
  CODEN =        "DANKAS",
  ISSN =         "0869-5652",
  MRclass =      "62L20 (15A18 15B51)",
  MRnumber =     "MR2573029",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "English translation in Dokl. Math. 79(3) 424--427
                 (2009).",
  acknowledgement = ack-nhfb,
  fjournal =     "Rossi\u\i skaya Akademiya Nauk. Doklady Akademii
                 Nauk",
}

@InProceedings{Phuoc:2009:PVK,
  author =       "Nguyen Quang Phuoc and Sung-Ryul Kim and Han-Ku Lee
                 and Hyung Seok Kim",
  booktitle =    "{ICCIT '09: Fourth International Conference on
                 Computer Sciences and Convergence Information
                 Technology (2009)}",
  title =        "{PageRank} vs. {Katz Status Index}, a Theoretical
                 Approach",
  crossref =     "Sohn:2009:FIC",
  pages =        "1276--1279",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/ICCIT.2009.272",
  ISBN =         "0-7695-3896-7",
  ISBN-13 =      "978-0-7695-3896-9",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5368419",
  abstract =     "In World Wide Web search engines, it is important to
                 have a good ranking system. One of the most famous
                 ranking components is the PageRank system by Google.
                 However, PageRank is protected by patents and it is
                 impossible for other companies to use it in their
                 search engines. There is an old model, called Katz
                 status index, that is reported to work very similar to
                 PageRank. If the quality of Katz status index turns out
                 to be similar to or better than that of PageRank, it
                 could become a patent-free alternative to PageRank. We
                 consider the problem of comparing Katz status index to
                 PageRank in this paper with some preliminary results on
                 the theoretical comparison and give a proposal for
                 practical comparison of the two models.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5367867",
  keywords =     "Katz status index; RageRank; search engines; World
                 Wide Web",
}

@InCollection{Rousseau:2009:GAP,
  author =       "Christiane Rousseau and Yvan Saint-Aubin",
  title =        "{Google} et l'algorithme {PageRank}",
  crossref =     "Rousseau:2009:MT",
  pages =        "273--297",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1007/978-0-387-69213-5_9",
  bibdate =      "Tue Jul 20 16:43:36 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  language =     "French",
}

@InProceedings{Su:2009:PHI,
  author =       "Cheng Su and YunTao Pan and JunPeng Yuan and Hong Guo
                 and ZhengLu Yu and ZhiYu Hu",
  booktitle =    "{2009 WRI World Congress on Computer Science and
                 Information Engineering}",
  title =        "{PageRank} , {HITS} and Impact Factor for Journal
                 Ranking",
  crossref =     "IEEE:2009:PWW",
  volume =       "6",
  pages =        "285--290",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/CSIE.2009.351",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5170706",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5170260",
}

@InProceedings{Wan:2009:IPA,
  author =       "Jing Wan and Si-Xue Bai",
  booktitle =    "{GRC '09: IEEE International Conference on Granular
                 Computing (2009)}",
  title =        "An improvement of {PageRank} algorithm based on the
                 time-activity-curve",
  crossref =     "Zhang:2006:IIC",
  pages =        "549--552",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/GRC.2009.5255060",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5255060",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5234367",
}

@Article{Wills:2009:ORG,
  author =       "Rebecca S. Wills and Ilse C. F. Ipsen",
  title =        "Ordinal Ranking for {Google}'s {PageRank}",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "30",
  number =       "4",
  pages =        "1677--1696",
  month =        "????",
  year =         "2009",
  CODEN =        "SJMAEL",
  DOI =          "http://dx.doi.org/10.1137/070698129",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  MRclass =      "62F07 (65F15 68P20)",
  MRnumber =     "2486859 (2010d:62041)",
  MRreviewer =   "Truc Nguyen",
  bibdate =      "Tue May 18 22:32:31 MDT 2010",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toclist/SIMAX/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.math.utah.edu/pub/bibnet/authors/i/ipsen-ilse-c-f.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
}

@Article{Xiong:2009:ESR,
  author =       "Zhiping Xiong and Bing Zheng",
  title =        "On the eigenvalues of a specially rank-$r$ updated
                 complex matrix",
  journal =      j-COMP-MATH-APPL,
  volume =       "57",
  number =       "10",
  pages =        "1645--1650",
  month =        may,
  year =         "2009",
  CODEN =        "CMAPDK",
  DOI =          "http://dx.doi.org/10.1016/j.camwa.2009.02.027",
  ISSN =         "0898-1221 (print), 1873-7668 (electronic)",
  ISSN-L =       "0898-1221",
  bibdate =      "Thu Dec 29 08:16:04 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0898122109002247",
  acknowledgement = ack-nhfb,
  fjournal =     "Computers and Mathematics with Applications",
  keywords =     "PageRank",
}

@InProceedings{Yen:2009:API,
  author =       "Chia-Chen Yen and Jih-Shih Hsu",
  editor =       "{IEEE}",
  booktitle =    "{VECIMS '09: IEEE International Conference on Virtual
                 Environments, Human-Computer Interfaces and
                 Measurements Systems (2009), May 11--13, 2009, Hong
                 Kong, China}",
  title =        "Associated {PageRank}: Improved {PageRank} measured by
                 frequent term sets",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "282--286",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/VECIMS.2009.5068909",
  ISBN =         "1-4244-3808-X",
  ISBN-13 =      "978-1-4244-3808-2",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5068909",
  abstract =     "Web search engines encounter many new challenges while
                 the amount of information on the web increases rapidly.
                 Web documents have been a main resource for various
                 purposes, and people rely on search engines to retrieve
                 the desired documents. This paper proposes an
                 associated pagerank algorithm for search engines to
                 feedback quality results by scoring the relevance of
                 web documents. The modified Pagerank algorithm
                 increases the degree of relevance than the original
                 one, and decreases the query time efforts of
                 topic-sensitive pagerank.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5038837",
  keywords =     "document relevance; information retrieval; pagerank;
                 topic-sensitive; web search",
}

@InProceedings{Yen:2009:PAI,
  author =       "Chia-Chen Yen and Jih-Shih Hsu",
  booktitle =    "{FUZZ-IEEE 2009: IEEE International Conference on
                 Fuzzy Systems}",
  title =        "{PageRank} algorithm improvement by page relevance
                 measurement",
  crossref =     "IEEE:2009:IIC",
  pages =        "502--506",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/FUZZY.2009.5277414",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5277414",
  abstract =     "Pagerank algorithm evaluates the importance of web
                 pages by the link analysis, and there are many
                 techniques to improve the traditional pagerank
                 algorithm to prevent from the biases of link spamming
                 in recent years. The modified algorithms should concern
                 not only the correctness, but also the efficiency
                 should be considered. This paper proposes an associated
                 pagerank algorithm for search engines to feedback
                 quality results by scoring the relevance between web
                 documents. The modified Pagerank algorithm increases
                 the degree of relevance than the original one, and
                 decreases the query time efforts of topic-sensitive
                 pagerank.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5247842",
  keywords =     "document relevance; information retrieval; pagerank;
                 topic-sensitive; Web search",
}

@InProceedings{Zhang:2009:IPW,
  author =       "Ling Zhang and Zheng Qin",
  booktitle =    "{2009 1st International Conference on Information
                 Science and Engineering (ICISE)}",
  title =        "The Improved {Pagerank} in {Web} Crawler",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "1889--1892",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/ICISE.2009.1220",
  ISBN =         "1-4244-4909-X",
  ISBN-13 =      "978-1-4244-4909-5",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5455065",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5454173",
}

@InProceedings{Zheng:2009:LSP,
  author =       "Ling Zheng and Ning Zhang and Yang Bo",
  editor =       "{IEEE}",
  booktitle =    "{ICISE '09: Proceedings of the 2009 First IEEE
                 International Conference on Information Science and
                 Engineering}",
  title =        "Link-Sensitive {PageRank}: An Improved Ranking
                 Algorithm for Vertical Search Engines",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "887--890",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1109/ICISE.2009.715",
  ISBN =         "0-7695-3887-8",
  ISBN-13 =      "978-0-7695-3887-7",
  LCCN =         "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5455348",
  abstract =     "The PageRank algorithm is an important link-based
                 ranking strategy of vertical search engines, but it has
                 a drawback of topic drift. To tackle this problem and
                 yield more accurate search results, we present an
                 improved algorithm to distribute the PageRank value in
                 light of the link sensitive level of the web pages
                 based on keywords set, which we called 'Link-Sensitive
                 PageRank'. According to the keywords of user's
                 searching, this algorithm, which takes into account the
                 link sensitive level of the web pages' hyperlink to
                 give different importance to different hyperlinks.
                 Experiment results show that the improved PageRank
                 algorithm performs better than the standard PageRank.
                 Furthermore, it can effectively improve the 'topic
                 drift' and enhance the accuracy of information
                 collection. The proposed PageRank algorithm can have a
                 good application in the vertical search engines.",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5454173",
}

@Article{Altman:2010:AAP,
  author =       "Alon Altman and Moshe Tennenholtz",
  title =        "An axiomatic approach to personalized ranking
                 systems",
  journal =      j-J-ACM,
  volume =       "57",
  number =       "4",
  pages =        "26:1--26:35",
  month =        apr,
  year =         "2010",
  CODEN =        "JACOAH",
  DOI =          "http://doi.acm.org/10.1145/1734213.1734220",
  ISSN =         "0004-5411 (print), 1557-735X (electronic)",
  ISSN-L =       "0004-5411",
  bibdate =      "Thu Apr 29 13:26:36 MDT 2010",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Personalized ranking systems and trust systems are an
                 essential tool for collaboration in a multi-agent
                 environment. In these systems, trust relations between
                 many agents are aggregated to produce a personalized
                 trust rating of the agents. In this article, we
                 introduce the first extensive axiomatic study of this
                 setting, and explore a wide array of well-known and new
                 personalized ranking systems. We adapt several axioms
                 (basic criteria) from the literature on global ranking
                 systems to the context of personalized ranking systems,
                 and fully classify the set of systems that satisfy all
                 of these axioms. We further show that all these axioms
                 are necessary for this result.",
  acknowledgement = ack-nhfb,
  articleno =    "26",
  fjournal =     "Journal of the ACM",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J401",
  keywords =     "Advogato; Axiomatic approach; e-Bay reputation system;
                 epinions.com; manipulation; MoleTrust; OpenPGP;
                 PageRank; ranking systems; social networks",
}

@Article{Bahmani:2010:FIP,
  author =       "Bahman Bahmani and Abdur Chowdhury and Ashish Goel",
  title =        "Fast incremental and personalized {PageRank}",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "4",
  number =       "3",
  pages =        "173--184",
  month =        dec,
  year =         "2010",
  CODEN =        "????",
  ISSN =         "2150-8097",
  bibdate =      "Fri May 13 14:55:16 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In this paper, we analyze the efficiency of Monte
                 Carlo methods for incremental computation of PageRank,
                 personalized PageRank, and similar random walk based
                 methods (with focus on SALSA), on large-scale
                 dynamically evolving social networks. We assume that
                 the graph of friendships is stored in distributed
                 shared memory, as is the case for large social networks
                 such as Twitter.\par

                 For global PageRank, we assume that the social network
                 has $n$ nodes, and $m$ adversarially chosen edges
                 arrive in a random order. We show that with a reset
                 probability of $\epsilon$, the expected total work
                 needed to maintain an accurate estimate (using the
                 Monte Carlo method) of the PageRank of every node at
                 all times is $O(n \ln m / \epsilon^2)$. This is
                 significantly better than all known bounds for
                 incremental PageRank. For instance, if we naively
                 recompute the PageRanks as each edge arrives, the
                 simple power iteration method needs $\Omega(m^2 / \ln(1
                 / (1 - \epsilon)))$ total time and the Monte Carlo
                 method needs $O(m n / \epsilon)$ total time; both are
                 prohibitively expensive. We also show that we can
                 handle deletions equally efficiently.\par

                 We then study the computation of the top $k$
                 personalized PageRanks starting from a seed node,
                 assuming that personalized PageRanks follow a power-law
                 with exponent $< 1$. We show that if we store $R > q
                 \ln n$ random walks starting from every node for large
                 enough constant q (using the approach outlined for
                 global PageRank), then the expected number of calls
                 made to the distributed social network database is
                 $O(k/(R^{(1 - \alpha) / \alpha}))$. We also present
                 experimental results from the social networking site,
                 Twitter, verifying our assumptions and analyses. The
                 overall result is that this algorithm is fast enough
                 for real-time queries over a dynamic social network.",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
}

@InProceedings{Chen:2010:PSC,
  author =       "Yao Chen and Wenjun Xiong and Jinhu Lu and D. W. C.
                 Ho",
  booktitle =    "{2010 International Conference on Intelligent
                 Computing and Integrated Systems (ICISS)}",
  title =        "Pinning scheme for complex networks based on
                 {PageRank} Algorithm",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "709--712",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/ICISS.2010.5657148",
  ISBN =         "",
  ISBN-13 =      "",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5657148",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5643978",
}

@Book{Clifton:2010:AWM,
  author =       "Brian Clifton",
  title =        "Advanced {Web} metrics with {Google Analytics}",
  publisher =    pub-WILEY,
  address =      pub-WILEY:adr,
  edition =      "Second",
  pages =        "xxv + 501",
  year =         "2010",
  ISBN =         "0-470-56231-5",
  ISBN-13 =      "978-0-470-56231-4",
  LCCN =         "TK5105.885.G66 C55 2010eb",
  bibdate =      "Fri Jun 3 09:52:48 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  acknowledgement = ack-nhfb,
  subject =      "Google Analytics; Web usage mining; Internet users;
                 Statistics; Data processing",
}

@Book{Croft:2010:SEI,
  author =       "W. Bruce Croft and Donald Metzler and Trevor
                 Strohman",
  title =        "Search engines: information retrieval in practice",
  publisher =    "Pearson Education",
  address =      "Boston, MA, USA",
  pages =        "xxv + 524",
  year =         "2010",
  ISBN =         "0-13-136489-8 (paperback)",
  ISBN-13 =      "978-0-13-136489-9 (paperback)",
  LCCN =         "TK5105.884 CRO 2010",
  bibdate =      "Thu May 5 19:23:28 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 library.ox.ac.uk:210/ADVANCE",
  acknowledgement = ack-nhfb,
  subject =      "Search engines; Information storage and retrieval
                 systems; Information retrieval",
}

@TechReport{Franceschet:2010:PSS,
  author =       "Massimo Franceschet",
  title =        "{PageRank}: Stand on the shoulders of giants",
  type =         "Report",
  institution =  "Department of Mathematics and Computer Science,
                 University of Udine",
  address =      "Via delle Scienze 206, 33100 Udine, Italy",
  pages =        "21",
  year =         "2010",
  bibdate =      "Fri Feb 19 15:07:14 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://arxiv.org/pdf/1002.2858",
  abstract =     "PageRank is a Web page ranking technique that
                 radically changed the concepts of quality and truth of
                 information found on the web. The method was devel-
                 oped by Sergey Brin and Larry Page while studying at
                 Stanford University and is currently an important
                 ingredient of Google search engine. The main idea
                 behind PageRank is to determine the importance of a Web
                 page in terms of the very same notion of importance
                 assigned to the pages hyperlinking to it. In fact, this
                 thesis in not new, and has been previously successfully
                 exploited in different contexts. In this work, we
                 review the PageRank method and link it to some renowned
                 predecessors we have found in the fields of Web
                 information retrieval, bibliometrics, sociology, and
                 economics.",
  acknowledgement = ack-nhfb,
  keywords =     "bibliometrics; commodity pricing; PageRank; social
                 network analysis; Web information retrieval",
}

@Article{Gleich:2010:IOI,
  author =       "David F. Gleich and Andrew P. Gray and Chen Greif and
                 Tracy Lau",
  title =        "An Inner-Outer Iteration for Computing {PageRank}",
  journal =      j-SIAM-J-SCI-COMP,
  volume =       "32",
  number =       "1",
  pages =        "349--371",
  month =        "????",
  year =         "2010",
  CODEN =        "SJOCE3",
  DOI =          "http://dx.doi.org/10.1137/080727397",
  ISSN =         "1064-8275 (print), 1095-7197 (electronic)",
  ISSN-L =       "1064-8275",
  bibdate =      "Wed May 19 10:44:24 MDT 2010",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SISC/32/1;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "We present a new iterative scheme for PageRank
                 computation. The algorithm is applied to the linear
                 system formulation of the problem, using inner-outer
                 stationary iterations. It is simple, can be easily
                 implemented and parallelized, and requires minimal
                 storage overhead. Our convergence analysis shows that
                 the algorithm is effective for a crude inner tolerance
                 and is not sensitive to the choice of the parameters
                 involved. The same idea can be used as a
                 preconditioning technique for nonstationary schemes.
                 Numerical examples featuring matrices of dimensions
                 exceeding 100,000,000 in sequential and parallel
                 environments demonstrate the merits of our technique.
                 Our code is available online for viewing and testing,
                 along with several large scale examples.",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Scientific Computing",
  journal-URL =  "http://epubs.siam.org/sisc",
}

@InProceedings{He:2010:WBL,
  author =       "Xiaojun He and Yibing Li and Chunxiao Fan",
  booktitle =    "{2010 International Conference on E-Business and
                 E-Government (ICEE)}",
  title =        "{Web}-Based Links and Authoritative Content {Pagerank}
                 Improvement",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "5016--5019",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/ICEE.2010.1259",
  ISBN =         "0-7695-3997-1",
  ISBN-13 =      "978-0-7695-3997-3",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5592871",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5589107",
}

@Article{Ishii:2010:DRA,
  author =       "H. Ishii and R. Tempo",
  title =        "Distributed Randomized Algorithms for the {PageRank}
                 Computation",
  journal =      j-IEEE-TRANS-AUTOMAT-CONTR,
  volume =       "55",
  number =       "9",
  pages =        "1987--2002",
  month =        "????",
  year =         "2010",
  CODEN =        "IETAA9",
  DOI =          "http://dx.doi.org/10.1109/TAC.2010.2042984",
  ISSN =         "0018-9286",
  ISSN-L =       "0018-9286",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5411738",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9",
  fjournal =     "IEEE Transactions on Automatic Control",
}

@InProceedings{Ishii:2010:DRP,
  author =       "H. Ishii and R. Tempo and E. Bai",
  booktitle =    "{2010 49th IEEE Conference on Decision and Control
                 (CDC)}",
  title =        "Distributed randomized pagerank algorithms based on
                 web aggregation over unreliable channels",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "6602--6607",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/CDC.2010.5718041",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5718041",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5707200",
}

@Article{Jiang:2010:TRB,
  author =       "Wei Jiang and Gang Wu",
  title =        "A thick-restarted block {Arnoldi} algorithm with
                 modified {Ritz} vectors for large eigenproblems",
  journal =      j-COMP-MATH-APPL,
  volume =       "60",
  number =       "3",
  pages =        "873--889",
  month =        aug,
  year =         "2010",
  CODEN =        "CMAPDK",
  DOI =          "http://dx.doi.org/10.1016/j.camwa.2010.05.034",
  ISSN =         "0898-1221 (print), 1873-7668 (electronic)",
  ISSN-L =       "0898-1221",
  bibdate =      "Thu Dec 29 08:18:39 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0898122110003913",
  acknowledgement = ack-nhfb,
  fjournal =     "Computers and Mathematics with Applications",
}

@Book{Kamvar:2010:NAP,
  author =       "Sep Kamvar",
  title =        "Numerical algorithms for personalized search in
                 self-organizing information networks",
  publisher =    pub-PRINCETON,
  address =      pub-PRINCETON:adr,
  pages =        "xiv + 139",
  year =         "2010",
  ISBN =         "0-691-14503-2 (hardcover)",
  ISBN-13 =      "978-0-691-14503-7 (hardcover)",
  LCCN =         "ZA4460 .K36 2010",
  bibdate =      "Mon Jun 13 18:50:45 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  abstract =     "This book lays out the theoretical groundwork for
                 personalized search and reputation management, both on
                 the Web and in peer-to-peer and social networks.'' The
                 book develops scalable algorithms that exploit the
                 graphlike properties underlying personalized search and
                 reputation management, and delves into realistic
                 scenarios regarding web-scale data",
  acknowledgement = ack-nhfb,
  subject =      "Database searching; Mathematics; Information networks;
                 Content analysis (Communication); Self-organizing
                 systems; Data processing; Algorithms; Internet
                 searching",
  tableofcontents = "World Wide Web \\
                 PageRank \\
                 The second eigenvalue of the Google Matrix \\
                 The condition number of the pagerank problem \\
                 Extrapolation algorithms \\
                 Adaptive pagerank \\
                 BlockRank \\
                 P2P networks. Query-cycle simulator \\
                 EigenTrust \\
                 Adaptive P2P topologies",
}

@Article{Kurland:2010:PHS,
  author =       "Oren Kurland and Lillian Lee",
  title =        "{PageRank} without hyperlinks: {Structural} reranking
                 using links induced by language models",
  journal =      j-TOIS,
  volume =       "28",
  number =       "4",
  pages =        "18:1--18:??",
  month =        nov,
  year =         "2010",
  CODEN =        "ATISET",
  DOI =          "http://dx.doi.org/10.1145/1852102.1852104",
  ISSN =         "1046-8188",
  ISSN-L =       "0734-2047",
  bibdate =      "Tue Nov 23 10:24:49 MST 2010",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tois/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  articleno =    "18",
  fjournal =     "ACM Transactions on Information Systems (TOIS)",
}

@Book{Ledford:2010:GA,
  author =       "Jerri L. Ledford and Joe Teixeira and Mary E. Tyler",
  title =        "{Google Analytics}",
  publisher =    pub-WILEY,
  address =      pub-WILEY:adr,
  edition =      "Third",
  pages =        "xxvii + 404",
  year =         "2010",
  ISBN =         "0-470-53128-2, 0-470-87400-7",
  ISBN-13 =      "978-0-470-53128-0, 978-0-470-87400-4",
  LCCN =         "TK5105.885.G66 L43 2010eb",
  bibdate =      "Fri Jun 3 09:52:48 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  acknowledgement = ack-nhfb,
  subject =      "Google Analytics; Internet searching; Statistical
                 services; Web usage mining; Computer programs; Internet
                 users; Statistics; Data processing",
  tableofcontents = ". Part 1. Getting started with Google Analytics \\
                 Part 2. Analytics and site statistics : concepts and
                 methods \\
                 Part 3. Advanced implementation \\
                 Part 4. The reports",
}

@InProceedings{Liu:2010:KEU,
  author =       "Zhengyang Liu and Jianyi Liu and Wenbin Yao and Cong
                 Wang",
  booktitle =    "{2010 International Conference on E-Product E-Service
                 and E-Entertainment (ICEEE)}",
  title =        "Keyword Extraction Using {PageRank} on Synonym
                 Networks",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "1--4",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/ICEEE.2010.5660630",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5660630",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5660084",
}

@InProceedings{Liu:2010:OMP,
  author =       "Dongfei Liu and Yong Gong",
  booktitle =    "{2010 2nd International Conference on Computer
                 Engineering and Technology (ICCET)}",
  title =        "Optimal methods of {PageRank} algorithm on the
                 bilingual web page",
  volume =       "1",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "V1--689--V1--691",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/ICCET.2010.5485388",
  ISBN =         "1-4244-6347-5",
  ISBN-13 =      "978-1-4244-6347-3",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5485388",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5473895",
}

@InProceedings{Ma:2010:RPA,
  author =       "Haibo Ma and Shiyong Chen and Deguang Wang",
  booktitle =    "{2010 International Conference on Web Information
                 Systems and Mining (WISM)}",
  title =        "Research of {PageRank} Algorithm Based on Transition
                 Probability",
  volume =       "1",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "153--155",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/WISM.2010.63",
  ISBN =         "1-4244-8438-3",
  ISBN-13 =      "978-1-4244-8438-6",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5662302",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5661667",
}

@InProceedings{McGettrick:2010:HCP,
  author =       "S. McGettrick and D. Geraghty",
  booktitle =    "{2010 International Conference on Reconfigurable
                 Computing and {FPGAs} (ReConFig)}",
  title =        "Hardware Computation of the {PageRank} Eigenvector",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "256--261",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/ReConFig.2010.83",
  ISBN =         "1-4244-9523-7",
  ISBN-13 =      "978-1-4244-9523-8",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5695315",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5692850",
}

@InProceedings{Nazin:2010:EPE,
  author =       "A. Nazin",
  booktitle =    "{2010 49th IEEE Conference on Decision and Control
                 (CDC)}",
  title =        "Estimating the principal eigenvector of a stochastic
                 matrix: Mirror Descent Algorithms via game approach
                 with application to {PageRank} problem",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "792--797",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/CDC.2010.5717923",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5717923",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5707200",
}

@InProceedings{Pu:2010:IPA,
  author =       "Bing-Yuan Pu and Ting-Zhu Huang and Chun Wen",
  booktitle =    "{2010 4th International Conference on Network and
                 System Security (NSS)}",
  title =        "An Improved {PageRank} Algorithm: Immune to Spam",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "425--429",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/NSS.2010.12",
  ISBN =         "1-4244-8484-7",
  ISBN-13 =      "978-1-4244-8484-3",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5635820",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5634608",
}

@InProceedings{Qin:2010:BRA,
  author =       "Yongbin Qin and Daoyun Xu",
  booktitle =    "{2010 2nd International Workshop on Intelligent
                 Systems and Applications (ISA)}",
  title =        "A Balanced Rank Algorithm Based on {PageRank} and Page
                 Belief Recommendation",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "1--4",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/IWISA.2010.5473657",
  ISBN =         "1-4244-5872-2",
  ISBN-13 =      "978-1-4244-5872-1",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5473657",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5472913",
}

@Book{Rhodes:2010:CLB,
  editor =       "Brett D. Rhodes",
  title =        "Copyright law and a brief look at the {Google Library
                 Project}",
  publisher =    "Nova Science Publishers",
  address =      "New York, NY, USA",
  pages =        "xi + 166",
  year =         "2010",
  ISBN =         "1-60741-871-1 (hardcover)",
  ISBN-13 =      "978-1-60741-871-9 (hardcover)",
  LCCN =         "KF2994 .C62 2010",
  bibdate =      "Fri Jun 3 09:47:20 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  series =       "Laws and legislation",
  acknowledgement = ack-nhfb,
  subject =      "Copyright; United States; Fair use (Copyright)",
  tableofcontents = "Copyright law : second edition / Robert A. Gorman,
                 Kenneth W. Gemmill \\
                 The Google Library Project : is digitization for
                 purposes of online indexing fair use under copyright
                 law? / Kate M. Manuel \\
                 Internet search engines : copyright's ``fair use'' in
                 reproduction and public display rights / Robin Jeweler,
                 Brian T. Yeh",
}

@InProceedings{Wang:2010:APA,
  author =       "Deguang Wang and Zhigang Zhou and Haibo Ma",
  booktitle =    "{2010 Second International Conference on Information
                 Technology and Computer Science (ITCS)}",
  title =        "Application of {PageRank} Algorithm in Computer
                 Forensics",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "250--253",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/ITCS.2010.68",
  ISBN =         "1-4244-7293-8",
  ISBN-13 =      "978-1-4244-7293-2",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5557139",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5556872",
}

@Article{Wu:2010:AVG,
  author =       "Gang Wu and Yimin Wei",
  title =        "{Arnoldi} versus {GMRES} for computing {PageRank}: {A}
                 theoretical contribution to {Google}'s {PageRank}
                 problem",
  journal =      j-TOIS,
  volume =       "28",
  number =       "3",
  pages =        "11:1--11:28",
  month =        jun,
  year =         "2010",
  CODEN =        "ATISET",
  DOI =          "http://doi.acm.org/10.1145/1777432.1777434",
  ISSN =         "1046-8188",
  ISSN-L =       "0734-2047",
  bibdate =      "Tue Jul 6 15:53:00 MDT 2010",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tois/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "PageRank is one of the most important ranking
                 techniques used in today's search engines. A recent
                 very interesting research track focuses on exploiting
                 efficient numerical methods to speed up the computation
                 of PageRank, among which the Arnoldi-type algorithm and
                 the GMRES algorithm are competitive candidates. In
                 essence, the former deals with the PageRank problem
                 from an eigenproblem, while the latter from a linear
                 system, point of view. However, there is little known
                 about the relations between the two approaches for
                 PageRank. In this article, we focus on a theoretical
                 and numerical comparison of the two approaches.
                 Numerical experiments illustrate the effectiveness of
                 our theoretical results.",
  acknowledgement = ack-nhfb,
  articleno =    "11",
  fjournal =     "ACM Transactions on Information Systems",
  keywords =     "Arnoldi; GMRES; Google; Krylov subspace; PageRank; Web
                 ranking",
}

@InProceedings{Wu:2010:EPS,
  author =       "Tianji Wu and Bo Wang and Yi Shan and Feng Yan and Yu
                 Wang and Ningyi Xu",
  booktitle =    "{2010 39th International Conference on Parallel
                 Processing (ICPP)}",
  title =        "Efficient {PageRank} and {SpMV} Computation on {AMD}
                 {GPUs}",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "81--89",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/ICPP.2010.17",
  ISBN =         "1-4244-7913-4",
  ISBN-13 =      "978-1-4244-7913-9",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5599152",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5598250",
}

@InProceedings{Zhang:2010:MSF,
  author =       "Yi Zhang and Kaihua Xu and Yuhua Liu and Zhenrong
                 Luo",
  editor =       "{IEEE}",
  booktitle =    "{Proceedings of the 2010 2nd International Conference
                 on Future Computer and Communication: ICFCC 2010, 21-24
                 May 2010, Wuhan, China}",
  title =        "Modeling of scale-free network based on pagerank
                 algorithm",
  volume =       "3",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "V3--783--V3--786",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/ICFCC.2010.5497402",
  ISBN =         "1-4244-5822-6, 1-4244-5821-8",
  ISBN-13 =      "978-1-4244-5822-6, 978-1-4244-5821-9",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "IEEE catalog number CFP1037G-PRT.",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5497402",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5487607",
}

@InProceedings{Zhang:2010:WRM,
  author =       "Ji-Lin Zhang and Yong-jian Ren and Wei Zhang and
                 Xiang-Hua Xu and Jian Wan and Yu Weng",
  booktitle =    "{2010 2nd International Conference on Information
                 Science and Engineering (ICISE)}",
  title =        "Webs ranking model based on pagerank algorithm",
  publisher =    "pub-IEEE",
  address =      "pub-IEEE:adr",
  pages =        "4811--4814",
  year =         "2010",
  DOI =          "http://dx.doi.org/10.1109/ICISE.2010.5691573",
  ISBN =         "1-4244-7616-X",
  ISBN-13 =      "978-1-4244-7616-9",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5691573",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5680733",
}

@Misc{Anonymous:2011:EOR,
  author =       "Anonymous",
  title =        "{{\tt eigenfactor.org}}: Ranking and mapping science",
  howpublished = "Web site.",
  year =         "2011",
  bibdate =      "Thu Jun 02 08:43:09 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "Journal impact ranking.",
  URL =          "http://www.eigenfactor.org/",
  acknowledgement = ack-nhfb,
}

@Book{Bailyn:2011:OG,
  author =       "Evan Bailyn and Brad Bailyn",
  title =        "Outsmarting Google",
  publisher =    pub-QUE,
  address =      pub-QUE:adr,
  pages =        "xi + 226",
  year =         "2011",
  ISBN =         "0-7897-4103-2",
  ISBN-13 =      "978-0-7897-4103-5",
  LCCN =         "HD9696.8.U64 G6627 2011",
  bibdate =      "Fri Jun 3 09:52:48 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  acknowledgement = ack-nhfb,
  subject =      "Electronic commerce; Internet searching; Web search
                 engines; Success in business",
  tableofcontents = "Trust: the currency of Google \\
                 The five ingredients of Google optimization \\
                 How to reel in links \\
                 Using time to gain trust \\
                 The nuclear football \\
                 Google AdWords as a complement to SEO \\
                 Tracking your progress with search operators \\
                 Google optimization myths \\
                 White hat versus black hat SEO \\
                 Optimizing for Yahoo! and Bing \\
                 Converting your SEO results into paying customers \\
                 The intersection of social media and SEO \\
                 The future of SEO.",
}

@InProceedings{Cailan:2011:IPA,
  author =       "Zhou Cailan and Chen Kai and Li Shasha",
  booktitle =    "{2011 International Conference on Computer Science and
                 Service System (CSSS)}",
  title =        "Improved {PageRank} algorithm based on feedback of
                 user clicks",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "3949--3952",
  year =         "2011",
  DOI =          "http://dx.doi.org/10.1109/CSSS.2011.5974627",
  bibdate =      "Mon Sep 12 21:28:08 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5974627",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5959270",
}

@Article{Cevahir:2011:SBP,
  author =       "Ali Cevahir and Cevdet Aykanat and Ata Turk and B.
                 Barla Cambazo{\u{g}}glu",
  title =        "Site-Based Partitioning and Repartitioning Techniques
                 for Parallel {PageRank} Computation",
  journal =      j-IEEE-TRANS-PAR-DIST-SYS,
  volume =       "22",
  number =       "5",
  pages =        "786--802",
  month =        may,
  year =         "2011",
  CODEN =        "ITDSEO",
  DOI =          "http://dx.doi.org/10.1109/TPDS.2010.119",
  ISSN =         "1045-9219 (print), 1558-2183 (electronic)",
  ISSN-L =       "1045-9219",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5482570",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=71",
  fjournal =     "IEEE Transactions on Parallel and Distributed
                 Systems",
  journal-URL =  "http://www.computer.org/portal/web/csdl/transactions/tpds",
}

@Article{Chakrabarti:2011:IDQ,
  author =       "Soumen Chakrabarti and Amit Pathak and Manish Gupta",
  title =        "Index design and query processing for graph
                 conductance search",
  journal =      j-VLDB-J,
  volume =       "20",
  number =       "3",
  pages =        "445--470",
  month =        jun,
  year =         "2011",
  CODEN =        "VLDBFR",
  DOI =          "http://dx.doi.org/10.1007/s00778-010-0204-8",
  ISSN =         "1066-8888 (print), 0949-877X (electronic)",
  ISSN-L =       "1066-8888",
  bibdate =      "Tue Jun 14 11:27:46 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.math.utah.edu/pub/tex/bib/vldbj.bib",
  abstract =     "Graph conductance queries, also known as personalized
                 PageRank and related to random walks with restarts,
                 were originally proposed to assign a hyperlink-based
                 prestige score to Web pages. More general forms of such
                 queries are also very useful for ranking in
                 entity-relation (ER) graphs used to represent
                 relational, XML and hypertext data. Evaluation of
                 PageRank usually involves a global eigen computation.
                 If the graph is even moderately large, interactive
                 response times may not be possible. Recently, the need
                 for interactive PageRank evaluation has increased. The
                 graph may be fully known only when the query is
                 submitted. Browsing actions of the user may change some
                 inputs to the PageRank computation dynamically.",
  acknowledgement = ack-nhfb,
  fjournal =     "VLDB Journal: Very Large Data Bases",
  journal-URL =  "http://portal.acm.org/toc.cfm?id=J869",
}

@Article{Dayar:2011:SSA,
  author =       "Tugrul Dayar and G{\"o}k{\c{c}}e N. Noyan",
  title =        "Steady-state analysis of {Google}-like stochastic
                 matrices with block iterative methods",
  journal =      j-ELECTRON-TRANS-NUMER-ANAL,
  volume =       "38",
  pages =        "69--97",
  year =         "2011",
  CODEN =        "????",
  ISSN =         "1068-9613 (print), 1097-4067 (electronic)",
  ISSN-L =       "1068-9613",
  bibdate =      "Thu Jun 9 12:14:22 MDT 2011",
  bibsource =    "http://etna.mcs.kent.edu/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "A Google-like matrix is a positive stochastic matrix
                 given by a convex combination of a sparse, nonnegative
                 matrix and a particular rank one matrix. Google itself
                 uses the steady-state vector of a large matrix of this
                 form to help order web pages in a search engine. We
                 investigate the computation of the steady-state vectors
                 of such matrices using block iterative methods. The
                 block partitionings considered include those based on
                 block triangular form and those having triangular
                 diagonal blocks obtained using cutsets. Numerical
                 results show that block Gauss-Seidel with partitionings
                 based on block triangular form is most often the best
                 approach. However, there are cases in which a block
                 partitioning with triangular diagonal blocks is better,
                 and the Gauss-Seidel method is usually competitive.",
  acknowledgement = ack-nhfb,
  fjournal =     "Electronic Transactions on Numerical Analysis",
  keywords =     "Google; PageRank; stochastic matrices; power method;
                 block iterative methods; partitionings; cutsets;
                 triangular blocks",
}

@Article{Franceschet:2011:PSS,
  author =       "Massimo Franceschet",
  title =        "{PageRank}: standing on the shoulders of giants",
  journal =      j-CACM,
  volume =       "54",
  number =       "6",
  pages =        "92--101",
  month =        jun,
  year =         "2011",
  CODEN =        "CACMA2",
  DOI =          "http://dx.doi.org/10.1145/1953122.1953146",
  ISSN =         "0001-0782 (print), 1557-7317 (electronic)",
  ISSN-L =       "0001-0782",
  bibdate =      "Wed Jun 1 18:12:20 MDT 2011",
  bibsource =    "http://www.acm.org/pubs/contents/journals/cacm/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "The roots of Google's PageRank can be traced back to
                 several early, and equally remarkable, ranking
                 techniques.",
  acknowledgement = ack-nhfb,
  fjournal =     "Communications of the ACM",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J79",
}

@Article{Greif:2011:NCS,
  author =       "Chen Greif and David Kurokawa",
  title =        "A Note on the Convergence of {SOR} for the {PageRank}
                 Problem",
  journal =      j-SIAM-J-SCI-COMP,
  volume =       "33",
  number =       "6",
  pages =        "3201--3209",
  month =        "????",
  year =         "2011",
  CODEN =        "SJOCE3",
  DOI =          "http://dx.doi.org/10.1137/110823523",
  ISSN =         "1064-8275 (print), 1095-7197 (electronic)",
  ISSN-L =       "1064-8275",
  bibdate =      "Thu Feb 9 06:05:59 MST 2012",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SISC/33/6;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib",
  URL =          "http://epubs.siam.org/sisc/resource/1/sjoce3/v33/i6/p3201_s1",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Scientific Computing",
  journal-URL =  "http://epubs.siam.org/sisc",
  onlinedate =   "November 08, 2011",
}

@InProceedings{Keong:2011:PMR,
  author =       "Boo Vooi Keong and Patricia Anthony",
  booktitle =    "{2011 7th International Conference on Information
                 Technology in Asia (CITA 11)}",
  title =        "{PageRank} : {A} modified random surfer model",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "1--6",
  year =         "2011",
  DOI =          "http://dx.doi.org/10.1109/CITA.2011.5998269",
  bibdate =      "Mon Sep 12 21:28:08 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5998269",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5984722",
}

@Article{Levene:2011:BRS,
  author =       "Mark Levene",
  title =        "Book Review: {Search Engines: Information Retrieval in
                 Practice}",
  journal =      j-COMP-J,
  volume =       "54",
  number =       "5",
  pages =        "831--832",
  month =        may,
  year =         "2011",
  CODEN =        "CMPJA6",
  DOI =          "http://dx.doi.org/10.1093/comjnl/bxq039",
  ISSN =         "0010-4620 (print), 1460-2067 (electronic)",
  ISSN-L =       "0010-4620",
  bibdate =      "Thu May 5 19:16:16 MDT 2011",
  bibsource =    "content/54/5.toc;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "See \cite{Croft:2010:SEI}.",
  URL =          "http://comjnl.oxfordjournals.org/content/54/5/831.full.pdf+html",
  acknowledgement = ack-nhfb,
  fjournal =     "The Computer Journal",
  journal-URL =  "http://comjnl.oxfordjournals.org/",
  keywords =     "Google; PageRank",
  onlinedate =   "April 13, 2010",
}

@Book{Levy:2011:PHG,
  author =       "Steven Levy",
  title =        "In the plex: how {Google} thinks, works, and shapes
                 our lives",
  publisher =    "Simon and Schuster",
  address =      "New York, NY, USA",
  pages =        "v + 424",
  year =         "2011",
  ISBN =         "1-4165-9658-5",
  ISBN-13 =      "978-1-4165-9658-5",
  LCCN =         "HD9696.8.U64 G6657 2011",
  bibdate =      "Fri Jun 3 09:45:37 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  abstract =     "Written with full cooperation from top management at
                 Google, this is the story behind the most successful
                 and admired technology company of our time.",
  acknowledgement = ack-nhfb,
  subject =      "Google; Internet industry; United States",
  tableofcontents = "The world according to Google: biography of a
                 search engine \\
                 Googlenomics: cracking the code on Internet profits \\
                 Don't be evil: how Google built its culture \\
                 Google's cloud: how Google built data centers and
                 killed the hard drive \\
                 Outside the box: the Google phone company. and the
                 Google t.v. company \\
                 Guge: Google moral dilemma in China \\
                 Google.gov: is what's good for Google, good for
                 government or the public? \\
                 Epilogue: chasing tail lights: trying to crack the
                 social code",
}

@Article{Menon:2011:FAA,
  author =       "Aditya Krishna Menon and Charles Elkan",
  title =        "Fast Algorithms for Approximating the Singular Value
                 Decomposition",
  journal =      j-TKDD,
  volume =       "5",
  number =       "2",
  pages =        "13:1--13:??",
  month =        feb,
  year =         "2011",
  CODEN =        "????",
  DOI =          "http://dx.doi.org/10.1145/1921632.1921639",
  ISSN =         "1556-4681 (print), 1556-472X (electronic)",
  ISSN-L =       "1556-4681",
  bibdate =      "Mon Mar 28 11:44:01 MDT 2011",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tkdd/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "A low-rank approximation to a matrix $A$ is a matrix
                 with significantly smaller rank than $A$, and which is
                 close to $A$ according to some norm. Many practical
                 applications involving the use of large matrices focus
                 on low-rank approximations. By reducing the rank or
                 dimensionality of the data, we reduce the complexity of
                 analyzing the data. The singular value decomposition is
                 the most popular low-rank matrix approximation.
                 However, due to its expensive computational
                 requirements, it has often been considered intractable
                 for practical applications involving massive data.
                 Recent developments have tried to address this problem,
                 with several methods proposed to approximate the
                 decomposition with better asymptotic runtime. We
                 present an empirical study of these techniques on a
                 variety of dense and sparse datasets. We find that a
                 sampling approach of Drineas, Kannan and Mahoney is
                 often, but not always, the best performing method. This
                 method gives solutions with high accuracy much faster
                 than classical SVD algorithms, on large sparse datasets
                 in particular. Other modern methods, such as a recent
                 algorithm by Rokhlin and Tygert, also offer savings
                 compared to classical SVD algorithms. The older
                 sampling methods of Achlioptas and McSherry are shown
                 to sometimes take longer than classical SVD.",
  acknowledgement = ack-nhfb,
  articleno =    "13",
  fjournal =     "ACM Transactions on Knowledge Discovery from Data
                 (TKDD)",
}

@Article{Sarma:2011:EPG,
  author =       "Atish Das Sarma and Sreenivas Gollapudi and Rina
                 Panigrahy",
  title =        "Estimating {PageRank} on graph streams",
  journal =      j-J-ACM,
  volume =       "58",
  number =       "3",
  pages =        "13:1--13:19",
  month =        may,
  year =         "2011",
  CODEN =        "JACOAH",
  DOI =          "http://dx.doi.org/10.1145/1970392.1970397",
  ISSN =         "0004-5411 (print), 1557-735X (electronic)",
  ISSN-L =       "0004-5411",
  bibdate =      "Fri Jun 3 18:12:24 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "This article focuses on computations on large graphs
                 (e.g., the web-graph) where the edges of the graph are
                 presented as a stream. The objective in the streaming
                 model is to use small amount of memory (preferably
                 sub-linear in the number of nodes $n$) and a smaller
                 number of passes.\par In the streaming model, we show
                 how to perform several graph computations including
                 estimating the probability distribution after a random
                 walk of length $l$, the mixing time $M$, and other
                 related quantities such as the conductance of the
                 graph. By applying our algorithm for computing
                 probability distribution on the web-graph, we can
                 estimate the PageRank $p$ of any node up to an additive
                 error of $\sqrt{\epsilon p} + \epsilon$ in
                 $\~O(\sqrt{M/\alpha})$ passes and $\~O(\min(n \alpha +
                 1 / \epsilon \sqrt{M / \alpha} + (1 / \epsilon) M
                 \alpha, \alpha n \sqrt{M \alpha} + (1 / \epsilon)
                 \sqrt{M / \alpha})) space, for any $\alpha \in
                 (0,1]$. Specifically, for $\epsilon = M / n$, $\alpha =
                 M^{-1/2}$, we can compute the approximate PageRank
                 values in $\~O(n M^{-1/4})$ space and $\~O(^M^{3/4})$
                 passes. In comparison, a standard implementation of the
                 PageRank algorithm will take $O(n)$ space and $O(M)$
                 passes. We also give an approach to approximate the
                 PageRank values in just $\~O(1)$ passes although this
                 requires $\~O(n M)$ space.",
  acknowledgement = ack-nhfb,
  articleno =    "13",
  fjournal =     "Journal of the ACM",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J401",
}

@InProceedings{Shen:2011:PAM,
  author =       "Xiaowei Shen and Xiwei Liu and Dong Fan and Changjian
                 Cheng and Gang Xiong",
  booktitle =    "{2011 IEEE International Conference on Service
                 Operations, Logistics, and Informatics (SOLI)}",
  title =        "A performance appraisal method based on {ACP} theory
                 and {PageRank} algorithm",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "197--201",
  year =         "2011",
  DOI =          "http://dx.doi.org/10.1109/SOLI.2011.5986555",
  bibdate =      "Mon Sep 12 21:28:08 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5986555",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5975308",
}

@Article{Tudisco:2011:PAP,
  author =       "Francesco Tudisco and Carmine {Di Fiore}",
  title =        "A preconditioning approach to the pagerank computation
                 problem",
  journal =      j-LINEAR-ALGEBRA-APPL,
  volume =       "435",
  number =       "9",
  pages =        "2222--2246",
  day =          "1",
  month =        nov,
  year =         "2011",
  CODEN =        "LAAPAW",
  DOI =          "http://dx.doi.org/10.1016/j.laa.2011.04.018",
  ISSN =         "0024-3795 (print), 1873-1856 (electronic)",
  ISSN-L =       "0024-3795",
  bibdate =      "Mon Jun 13 18:34:49 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/linala2010.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.sciencedirect.com/science/journal/00243795",
  acknowledgement = ack-nhfb,
  fjournal =     "Linear Algebra and its Applications",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00243795",
}

@Article{Yan:2011:FBA,
  author =       "Jing Yan and Ning-Yi Xu and Xiong-Fei Cai and Rui Gao
                 and Yu Wang and Rong Luo and Feng-Hsiung Hsu",
  title =        "An {FPGA}-based accelerator for {LambdaRank} in Web
                 search engines",
  journal =      j-TRETS,
  volume =       "4",
  number =       "3",
  pages =        "25:1--25:??",
  month =        aug,
  year =         "2011",
  CODEN =        "????",
  DOI =          "http://dx.doi.org/10.1145/2000832.2000837",
  ISSN =         "1936-7406 (print), 1936-7414 (electronic)",
  ISSN-L =       "1936-7406",
  bibdate =      "Tue Aug 30 08:13:57 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "In modern Web search engines, Neural Network
                 (NN)-based learning to rank algorithms is intensively
                 used to increase the quality of search results.
                 LambdaRank is one such algorithm. However, it is hard
                 to be efficiently accelerated by computer clusters or
                 GPUs, because: (i) the cost function for the ranking
                 problem is much more complex than that of traditional
                 Back-Propagation(BP) NNs, and (ii) no coarse-grained
                 parallelism exists in the algorithm. This article
                 presents an FPGA-based accelerator solution to provide
                 high computing performance with low power consumption.
                 A compact deep pipeline is proposed to handle the
                 complex computing in the batch updating. The area
                 scales linearly with the number of hidden nodes in the
                 algorithm. We also carefully design a data format to
                 enable streaming consumption of the training data from
                 the host computer. The accelerator shows up to 15.3X
                 (with PCIe x4) and 23.9X (with PCIe x8) speedup
                 compared with the pure software implementation on
                 datasets from a commercial search engine.",
  acknowledgement = ack-nhfb,
  articleno =    "25",
  fjournal =     "ACM Transactions on Reconfigurable Technology and
                 Systems (TRETS)",
  journal-URL =  "http://portal.acm.org/toc.cfm?id=J1151",
}

@InProceedings{Yan:2011:RPH,
  author =       "Lili Yan and Yingbin Wei and Zhanji Gui and Yizhuo
                 Chen",
  booktitle =    "{2011 International Conference on Internet Technology
                 and Applications (iTAP)}",
  title =        "Research on {PageRank} and Hyperlink-Induced Topic
                 Search in {Web} Structure Mining",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "1--4",
  year =         "2011",
  DOI =          "http://dx.doi.org/10.1109/ITAP.2011.6006308",
  bibdate =      "Mon Sep 12 21:28:08 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6006308",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6005185",
}

@InProceedings{Zha:2011:EIS,
  author =       "Peng Zha and Xiu Xu and Ming Zuo",
  booktitle =    "{2011 International Conference on Management and
                 Service Science (MASS)}",
  title =        "An Efficient Improved Strategy for the {PageRank}
                 Algorithm",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "1--4",
  year =         "2011",
  DOI =          "http://dx.doi.org/10.1109/ICMSS.2011.5999297",
  bibdate =      "Mon Sep 12 21:28:08 MDT 2011",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5999297",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5996071",
}

@Article{Agryzkov:2012:ARN,
  author =       "Taras Agryzkov and Jose L. Oliver and Leandro Tortosa
                 and Jose F. Vicent",
  title =        "An algorithm for ranking the nodes of an urban network
                 based on the concept of {PageRank} vector",
  journal =      j-APPL-MATH-COMP,
  volume =       "219",
  number =       "4",
  pages =        "2186--2193",
  day =          "1",
  month =        nov,
  year =         "2012",
  CODEN =        "AMHCBQ",
  DOI =          "http://dx.doi.org/10.1016/j.amc.2012.08.064",
  ISSN =         "0096-3003 (print), 1873-5649 (electronic)",
  ISSN-L =       "0096-3003",
  bibdate =      "Thu Oct 25 09:05:21 MDT 2012",
  bibsource =    "http://www.sciencedirect.com/science/journal/00963003;
                 http://www.math.utah.edu/pub/tex/bib/applmathcomput2010.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0096300312008570",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Mathematics and Computation",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00963003",
}

@Article{Borgs:2012:STA,
  author =       "Christian Borgs and Michael Brautbar",
  title =        "A Sublinear Time Algorithm for {PageRank}
                 Computations",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "7323",
  pages =        "41--53",
  year =         "2012",
  CODEN =        "LNCSD9",
  DOI =          "http://dx.doi.org/10.1007/978-3-642-30541-2_4",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  bibdate =      "Mon Dec 24 07:30:37 MST 2012",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/lncs2012e.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/accesspage/chapter/10.1007/978-3-642-30541-2_3;
                 http://link.springer.com/chapter/10.1007/978-3-642-30541-2_4/;
                 http://link.springer.com/content/pdf/10.1007/978-3-642-30541-2_4",
  acknowledgement = ack-nhfb,
  book-DOI =     "http://dx.doi.org/10.1007/978-3-642-30541-2",
  book-URL =     "http://www.springerlink.com/content/978-3-642-30541-2",
  fjournal =     "Lecture Notes in Computer Science",
}

@Article{Brin:2012:RAL,
  author =       "Sergey Brin and Lawrence Page",
  title =        "Reprint of: {The anatomy of a large-scale hypertextual
                 Web search engine}",
  journal =      j-COMP-NET-AMSTERDAM,
  volume =       "56",
  number =       "18",
  pages =        "3825--3833",
  day =          "17",
  month =        dec,
  year =         "2012",
  CODEN =        "????",
  DOI =          "http://dx.doi.org/10.1016/j.comnet.2012.10.007",
  ISSN =         "1389-1286 (print), 1872-7069 (electronic)",
  ISSN-L =       "1389-1286",
  bibdate =      "Fri Nov 30 12:26:39 MST 2012",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/compnetamsterdam2010.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.sciencedirect.com/science/journal/13891286",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1389128612003611",
  acknowledgement = ack-nhfb,
  fjournal =     "Computer Networks",
  journal-URL =  "http://www.sciencedirect.com/science/journal/13891286",
  keywords =     "PageRank algorithm",
}

@Article{Chung:2012:MCA,
  author =       "Fan Chung and Paul Horn and Jacob Hughes",
  title =        "Multi-commodity Allocation for Dynamic Demands Using
                 {PageRank} Vectors",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "7323",
  pages =        "138--152",
  year =         "2012",
  CODEN =        "LNCSD9",
  DOI =          "http://dx.doi.org/10.1007/978-3-642-30541-2_11",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  bibdate =      "Mon Dec 24 07:30:37 MST 2012",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/lncs2012e.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/content/pdf/10.1007/978-3-642-30541-2_11",
  acknowledgement = ack-nhfb,
  book-DOI =     "http://dx.doi.org/10.1007/978-3-642-30541-2",
  book-URL =     "http://www.springerlink.com/content/978-3-642-30541-2",
  fjournal =     "Lecture Notes in Computer Science",
}

@Article{Kumar:2012:PPM,
  author =       "Tarun Kumar and Parikshit Sondhi and Ankush Mittal",
  title =        "Parallelization of {PageRank} on Multicore
                 Processors",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "7154",
  pages =        "129--140",
  year =         "2012",
  CODEN =        "LNCSD9",
  DOI =          "http://dx.doi.org/10.1007/978-3-642-28073-3_12",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  bibdate =      "Mon Dec 24 07:16:06 MST 2012",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/lncs2012b.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/content/pdf/10.1007/978-3-642-28073-3_12",
  acknowledgement = ack-nhfb,
  book-DOI =     "http://dx.doi.org/10.1007/978-3-642-28073-3",
  book-URL =     "http://www.springerlink.com/content/978-3-642-28073-3",
  fjournal =     "Lecture Notes in Computer Science",
}

@Article{Liu:2012:IPA,
  author =       "Dian-Xing Liu and Xia Yan and Wei Xie",
  title =        "Improved {PageRank} Algorithm Based on the Residence
                 Time of the {Website}",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "7390",
  pages =        "601--607",
  year =         "2012",
  CODEN =        "LNCSD9",
  DOI =          "http://dx.doi.org/10.1007/978-3-642-31576-3_76",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  bibdate =      "Mon Dec 24 07:42:40 MST 2012",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/lncs2012f.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/content/pdf/10.1007/978-3-642-31576-3_76",
  acknowledgement = ack-nhfb,
  book-DOI =     "http://dx.doi.org/10.1007/978-3-642-31576-3",
  book-URL =     "http://www.springerlink.com/content/978-3-642-31576-3",
  fjournal =     "Lecture Notes in Computer Science",
}

@Book{MacCormick:2012:NAC,
  author =       "John MacCormick",
  title =        "Nine algorithms that changed the future: the ingenious
                 ideas that drive today's computers",
  publisher =    pub-PRINCETON,
  address =      pub-PRINCETON:adr,
  pages =        "x + 219",
  year =         "2012",
  ISBN =         "0-691-14714-0",
  ISBN-13 =      "978-0-691-14714-7",
  LCCN =         "QA76 .M21453 2012",
  bibdate =      "Thu Apr 19 14:55:06 MDT 2012",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  note =         "With a foreword by Chris Bishop.",
  abstract =     "Every day, we use our computers to perform remarkable
                 feats. A simple web search picks out a handful of
                 relevant needles from the world's biggest haystack: the
                 billions of pages on the World Wide Web. Uploading a
                 photo to Facebook transmits millions of pieces of
                 information over numerous error-prone network links,
                 yet somehow a perfect copy of the photo arrives intact.
                 Without even knowing it, we use public-key cryptography
                 to transmit secret information like credit card
                 numbers; and we use digital signatures to verify the
                 identity of the websites we visit. How do our computers
                 perform these tasks with such ease?\par

                 This is the first book to answer that question in
                 language anyone can understand, revealing the
                 extraordinary ideas that power our PCs, laptops, and
                 smartphones. Using vivid examples, John MacCormick
                 explains the fundamental ``tricks'' behind nine types
                 of computer algorithms, including artificial
                 intelligence (where we learn about the ``nearest
                 neighbor trick'' and ``twenty questions trick''),
                 Google's famous PageRank algorithm (which uses the
                 ``random surfer trick''), data compression, error
                 correction, and much more.\par

                 These revolutionary algorithms have changed our world:
                 this book unlocks their secrets, and lays bare the
                 incredible ideas that our computers use every day.",
  acknowledgement = ack-nhfb,
  subject =      "Computer science; Computer algorithms; Artificial
                 intelligence",
}

@Article{Rossi:2012:DPU,
  author =       "Ryan A. Rossi and David F. Gleich",
  title =        "Dynamic {PageRank} Using Evolving Teleportation",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "7323",
  pages =        "126--137",
  year =         "2012",
  CODEN =        "LNCSD9",
  DOI =          "http://dx.doi.org/10.1007/978-3-642-30541-2_10",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  bibdate =      "Mon Dec 24 07:30:37 MST 2012",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/lncs2012e.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/content/pdf/10.1007/978-3-642-30541-2_10",
  acknowledgement = ack-nhfb,
  book-DOI =     "http://dx.doi.org/10.1007/978-3-642-30541-2",
  book-URL =     "http://www.springerlink.com/content/978-3-642-30541-2",
  fjournal =     "Lecture Notes in Computer Science",
}

@Article{Wu:2012:PSG,
  author =       "Gang Wu and Yan-Chun Wang and Xiao-Qing Jin",
  title =        "A Preconditioned and Shifted {GMRES} Algorithm for the
                 {PageRank} Problem with Multiple Damping Factors",
  journal =      j-SIAM-J-SCI-COMP,
  volume =       "34",
  number =       "5",
  pages =        "A2558--A2575",
  month =        "????",
  year =         "2012",
  CODEN =        "SJOCE3",
  DOI =          "http://dx.doi.org/10.1137/110834585",
  ISSN =         "1064-8275 (print), 1095-7197 (electronic)",
  ISSN-L =       "1064-8275",
  bibdate =      "Tue Oct 30 14:49:10 MDT 2012",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SISC/34/5;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Scientific Computing",
  journal-URL =  "http://epubs.siam.org/sisc",
  onlinedate =   "January 2012",
}

@Article{Yin:2012:AAA,
  author =       "Jun-Feng Yin and Guo-Jian Yin and Michael Ng",
  title =        "On adaptively accelerated {Arnoldi} method for
                 computing {PageRank}",
  journal =      j-NUM-LIN-ALG-APPL,
  volume =       "19",
  number =       "1",
  pages =        "73--85",
  month =        jan,
  year =         "2012",
  CODEN =        "NLAAEM",
  DOI =          "http://dx.doi.org/10.1002/nla.789",
  ISSN =         "1070-5325 (print), 1099-1506 (electronic)",
  ISSN-L =       "1070-5325",
  bibdate =      "Fri Mar 16 18:11:23 MDT 2012",
  bibsource =    "http://www3.interscience.wiley.com/journalfinder.html;
                 http://www.interscience.wiley.com/jpages/1070-5325;
                 http://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Numerical Linear Algebra with Applications",
  journal-URL =  "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1506",
  onlinedate =   "20 Nov 2011",
}

@Article{Zhang:2012:AKR,
  author =       "Weinan Zhang and Dingquan Wang and Gui-Rong Xue and
                 Hongyuan Zha",
  title =        "Advertising Keywords Recommendation for Short-Text
                 {Web} Pages Using {Wikipedia}",
  journal =      j-TIST,
  volume =       "3",
  number =       "2",
  pages =        "36:1--36:??",
  month =        feb,
  year =         "2012",
  CODEN =        "????",
  DOI =          "http://dx.doi.org/10.1145/2089094.2089112",
  ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
  ISSN-L =       "2157-6904",
  bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.math.utah.edu/pub/tex/bib/tist.bib",
  abstract =     "Advertising keywords recommendation is an
                 indispensable component for online advertising with the
                 keywords selected from the target Web pages used for
                 contextual advertising or sponsored search. Several
                 ranking-based algorithms have been proposed for
                 recommending advertising keywords. However, for most of
                 them performance is still lacking, especially when
                 dealing with short-text target Web pages, that is,
                 those containing insufficient textual information for
                 ranking. In some cases, short-text Web pages may not
                 even contain enough keywords for selection. A natural
                 alternative is then to recommend relevant keywords not
                 present in the target Web pages. In this article, we
                 propose a novel algorithm for advertising keywords
                 recommendation for short-text Web pages by leveraging
                 the contents of Wikipedia, a user-contributed online
                 encyclopedia. Wikipedia contains numerous entities with
                 related entities on a topic linked to each other. Given
                 a target Web page, we propose to use a content-biased
                 PageRank on the Wikipedia graph to rank the related
                 entities. Furthermore, in order to recommend
                 high-quality advertising keywords, we also add an
                 advertisement-biased factor into our model. With these
                 two biases, advertising keywords that are both relevant
                 to a target Web page and valuable for advertising are
                 recommended. In our experiments, several
                 state-of-the-art approaches for keyword recommendation
                 are compared. The experimental results demonstrate that
                 our proposed approach produces substantial improvement
                 in the precision of the top 20 recommended keywords on
                 short-text Web pages over existing approaches.",
  acknowledgement = ack-nhfb,
  articleno =    "36",
  fjournal =     "ACM Transactions on Intelligent Systems and Technology
                 (TIST)",
}

@Article{Garcia:2013:LPP,
  author =       "E. Garc{\'\i}a and F. Pedroche and M. Romance",
  title =        "On the localization of the personalized {PageRank} of
                 complex networks",
  journal =      j-LINEAR-ALGEBRA-APPL,
  volume =       "439",
  number =       "3",
  pages =        "640--652",
  day =          "1",
  month =        aug,
  year =         "2013",
  CODEN =        "LAAPAW",
  ISSN =         "0024-3795 (print), 1873-1856 (electronic)",
  ISSN-L =       "0024-3795",
  bibdate =      "Mon Jun 24 07:02:58 MDT 2013",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/linala2010.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.sciencedirect.com/science/journal/00243795",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0024379512007835",
  acknowledgement = ack-nhfb,
  fjournal =     "Linear Algebra and its Applications",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00243795",
}

@Article{Wu:2013:AAT,
  author =       "Gang Wu and Ying Zhang and Yimin Wei",
  title =        "Accelerating the {Arnoldi}-Type Algorithm for the
                 {PageRank} Problem and the {ProteinRank} Problem",
  journal =      j-J-SCI-COMPUT,
  volume =       "57",
  number =       "1",
  pages =        "74--104",
  month =        oct,
  year =         "2013",
  CODEN =        "JSCOEB",
  DOI =          "http://dx.doi.org/10.1007/s10915-013-9696-x",
  ISSN =         "0885-7474 (print), 1573-7691 (electronic)",
  ISSN-L =       "0885-7474",
  bibdate =      "Sat Mar 8 11:16:24 MST 2014",
  bibsource =    "http://link.springer.com/journal/10915;
                 http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0885-7474&volume=57&issue=1;
                 http://www.math.utah.edu/pub/tex/bib/jscicomput.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/article/10.1007/s10915-013-9696-x;
                 http://link.springer.com/content/pdf/10.1007/s10915-013-9696-x.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Scientific Computing",
}

@Article{Zhang:2013:RWM,
  author =       "Zhu Zhang and Daniel D. Zeng and Ahmed Abbasi and Jing
                 Peng and Xiaolong Zheng",
  title =        "A Random Walk Model for Item Recommendation in Social
                 Tagging Systems",
  journal =      j-TMIS,
  volume =       "4",
  number =       "2",
  pages =        "8:1--8:??",
  month =        aug,
  year =         "2013",
  CODEN =        "????",
  DOI =          "http://dx.doi.org/10.1145/2490860",
  ISSN =         "2158-656X",
  bibdate =      "Thu Mar 13 06:54:56 MDT 2014",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tmis/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.math.utah.edu/pub/tex/bib/tmis.bib",
  abstract =     "Social tagging, as a novel approach to information
                 organization and discovery, has been widely adopted in
                 many Web 2.0 applications. Tags contributed by users to
                 annotate a variety of Web resources or items provide a
                 new type of information that can be exploited by
                 recommender systems. Nevertheless, the sparsity of the
                 ternary interaction data among users, items, and tags
                 limits the performance of tag-based recommendation
                 algorithms. In this article, we propose to deal with
                 the sparsity problem in social tagging by applying
                 random walks on ternary interaction graphs to explore
                 transitive associations between users and items. The
                 transitive associations in this article refer to the
                 path of the link between any two nodes whose length is
                 greater than one. Taking advantage of these transitive
                 associations can allow more accurate measurement of the
                 relevance between two entities (e.g., user-item,
                 user-user, and item-item). A PageRank-like algorithm
                 has been developed to explore these transitive
                 associations by spreading users' preferences on an item
                 similarity graph and spreading items' influences on a
                 user similarity graph. Empirical evaluation on three
                 real-world datasets demonstrates that our approach can
                 effectively alleviate the sparsity problem and improve
                 the quality of item recommendation.",
  acknowledgement = ack-nhfb,
  articleno =    "8",
  fjournal =     "ACM Transactions on Management Information Systems
                 (TMIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1320",
}

@Article{Zhu:2013:IAA,
  author =       "Fanwei Zhu and Yuan Fang and Kevin Chen-Chuan Chang
                 and Jing Ying",
  title =        "Incremental and accuracy-aware {Personalized PageRank}
                 through scheduled approximation",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "6",
  number =       "6",
  pages =        "481--492",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  ISSN =         "2150-8097",
  bibdate =      "Fri Dec 13 05:56:32 MST 2013",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  abstract =     "As Personalized PageRank has been widely leveraged for
                 ranking on a graph, the efficient computation of
                 Personalized PageRank Vector (PPV) becomes a prominent
                 issue. In this paper, we propose FastPPV, an
                 approximate PPV computation algorithm that is
                 incremental and accuracy-aware. Our approach hinges on
                 a novel paradigm of scheduled approximation: the
                 computation is partitioned and scheduled for processing
                 in an ``organized'' way, such that we can gradually
                 improve our PPV estimation in an incremental manner,
                 and quantify the accuracy of our approximation at query
                 time. Guided by this principle, we develop an efficient
                 hub based realization, where we adopt the metric of
                 hub-length to partition and schedule random walk tours
                 so that the approximation error reduces exponentially
                 over iterations. Furthermore, as tours are segmented by
                 hubs, the shared substructures between different tours
                 (around the same hub) can be reused to speed up query
                 processing both within and across iterations. Finally,
                 we evaluate FastPPV over two real-world graphs, and
                 show that it not only significantly outperforms two
                 state-of-the-art baselines in both online and offline
                 phrases, but also scale well on larger graphs. In
                 particular, we are able to achieve near-constant time
                 online query processing irrespective of graph size.",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
}

@Article{Lofgren:2014:CMC,
  author =       "Peter Lofgren",
  title =        "On the complexity of the {Monte Carlo} method for
                 incremental {PageRank}",
  journal =      j-INFO-PROC-LETT,
  volume =       "114",
  number =       "3",
  pages =        "104--106",
  month =        mar,
  year =         "2014",
  CODEN =        "IFPLAT",
  ISSN =         "0020-0190 (print), 1872-6119 (electronic)",
  ISSN-L =       "0020-0190",
  bibdate =      "Mon Dec 9 09:33:47 MST 2013",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/infoproc2010.bib;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.sciencedirect.com/science/journal/00200190",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0020019013002743",
  acknowledgement = ack-nhfb,
  fjournal =     "Information Processing Letters",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00200190",
}

%%% ====================================================================
%%% Cross-referenced entries must come last:

@Proceedings{ACM:2001:CPT,
  editor =       "{ACM}",
  booktitle =    "{Conference proceedings: the Tenth International World
                 Wide Web Conference, Hong Kong, May 1--5, 2001}",
  title =        "{Conference proceedings: the Tenth International World
                 Wide Web Conference, Hong Kong, May 1--5, 2001}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "xxii + 770",
  year =         "2001",
  ISBN =         "1-58113-348-0",
  ISBN-13 =      "978-1-58113-348-6",
  LCCN =         "TK5105.888 .I573 2001",
  bibdate =      "Mon May 10 14:10:25 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  URL =          "http://portal.acm.org/toc.cfm?id=511446",
  acknowledgement = ack-nhfb,
  meetingname =  "International WWW Conference (10th: 2001: Hong Kong,
                 China)",
  subject =      "World Wide Web; Congresses",
}

@Proceedings{Bahill:2001:IIC,
  editor =       "Terry Bahill",
  booktitle =    "{2001 IEEE International Conference on Systems, Man
                 and Cybernetics: October 7--10, 2001, Tucson Convention
                 Center, Tucson, Arizona, USA}",
  title =        "{2001 IEEE International Conference on Systems, Man
                 and Cybernetics: October 7--10, 2001, Tucson Convention
                 Center, Tucson, Arizona, USA}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2001",
  ISBN =         "0-7803-7087-2, 0-7803-7088-0 (microfiche),
                 0-7803-7089-9 (CD-ROM)",
  ISBN-13 =      "978-0-7803-7087-6, 978-0-7803-7088-3 (microfiche),
                 978-0-7803-7089-0 (CD-ROM)",
  LCCN =         "TA168 .I18 2001",
  bibdate =      "Thu May 6 13:33:15 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  note =         "IEEE catalog number 01CH37236.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=7658",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE International Conference on Systems, Man, and
                 Cybernetics (2001: Tucson, Ariz.)",
  subject =      "Cybernetics; Congresses; Systems engineering;
                 Human-machine systems",
}

@Proceedings{Croft:2001:PAI,
  editor =       "W. Bruce Croft and others",
  booktitle =    "{Proceedings of the 24th Annual International ACM
                 SIGIR Conference on Research and Development in
                 Information Retrieval: New Orleans, Louisiana, USA,
                 September 9--13, 2001}",
  title =        "{Proceedings of the 24th Annual International ACM
                 SIGIR Conference on Research and Development in
                 Information Retrieval: New Orleans, Louisiana, USA,
                 September 9--13, 2001}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "xvi + 464",
  year =         "2001",
  ISBN =         "1-58113-331-6",
  ISBN-13 =      "978-1-58113-331-8",
  LCCN =         "QA76.9.D3 I552 2001",
  bibdate =      "Wed Jun 1 18:28:55 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "ACM order number 606010. Special issue of the SIGIR
                 Forum, {\bf 24} (2001).",
  acknowledgement = ack-nhfb,
}

@Proceedings{Anonymous:2002:PIW,
  editor =       "Anonymous",
  booktitle =    "{Proceedings of the 11th International World Wide Web
                 Conference: Sheraton Waikiki, Honolulu, Hawaii, 7--11
                 May 2002. WWW 2002}",
  title =        "{Proceedings of the 11th International World Wide Web
                 Conference: Sheraton Waikiki, Honolulu, Hawaii, 7--11
                 May 2002}. {WWW} 2002",
  publisher =    "????",
  address =      "Honolulu, HI, USA",
  pages =        "????",
  year =         "2002",
  ISBN =         "1-880672-20-0",
  ISBN-13 =      "978-1-880672-20-4",
  LCCN =         "????",
  bibdate =      "Thu May 6 11:07:50 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  acknowledgement = ack-nhfb,
}

@Article{Mcmillan:2013:PSR,
  author =       "Collin Mcmillan and Denys Poshyvanyk and Mark
                 Grechanik and Qing Xie and Chen Fu",
  title =        "{Portfolio}: Searching for relevant functions and
                 their usages in millions of lines of code",
  journal =      j-TOSEM,
  volume =       "22",
  number =       "4",
  pages =        "37:1--37:??",
  month =        oct,
  year =         "2013",
  CODEN =        "ATSMER",
  DOI =          "http://dx.doi.org/10.1145/2522920.2522930",
  ISSN =         "1049-331X (print), 1557-7392 (electronic)",
  ISSN-L =       "1049-331X",
  bibdate =      "Wed Oct 30 12:18:03 MDT 2013",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tosem/;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.math.utah.edu/pub/tex/bib/tosem.bib",
  abstract =     "Different studies show that programmers are more
                 interested in finding definitions of functions and
                 their uses than variables, statements, or ordinary code
                 fragments. Therefore, developers require support in
                 finding relevant functions and determining how these
                 functions are used. Unfortunately, existing code search
                 engines do not provide enough of this support to
                 developers, thus reducing the effectiveness of code
                 reuse. We provide this support to programmers in a code
                 search system called Portfolio that retrieves and
                 visualizes relevant functions and their usages. We have
                 built Portfolio using a combination of models that
                 address surfing behavior of programmers and sharing
                 related concepts among functions. We conducted two
                 experiments: first, an experiment with 49 C/C++
                 programmers to compare Portfolio to Google Code Search
                 and Koders using a standard methodology for evaluating
                 information-retrieval-based engines; and second, an
                 experiment with 19 Java programmers to compare
                 Portfolio to Koders. The results show with strong
                 statistical significance that users find more relevant
                 functions with higher precision with Portfolio than
                 with Google Code Search and Koders. We also show that
                 by using PageRank, Portfolio is able to rank returned
                 relevant functions more efficiently.",
  acknowledgement = ack-nhfb,
  articleno =    "37",
  fjournal =     "ACM Transactions on Software Engineering and
                 Methodology",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J790",
  keywords =     "PageRank algorithm",
}

@Proceedings{WangLing:2002:PTI,
  editor =       "Tok {Wang Ling} and others",
  booktitle =    "{Proceedings of the Third International Conference on
                 Web Information Systems Engineering: Singapore, 12--14
                 December, 2002}",
  title =        "{Proceedings of the Third International Conference on
                 Web Information Systems Engineering: Singapore, 12--14
                 December, 2002}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "373",
  year =         "2002",
  ISBN =         "0-7695-1766-8",
  ISBN-13 =      "978-0-7695-1766-7",
  LCCN =         "TA168 .I583 200",
  bibdate =      "Thu May 6 13:57:37 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number PR01768.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=8419",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Web Information Systems
                 Engineering (3rd: 2002: Singapore)",
  subject =      "World Wide Web; Congresses; Internet; Systems
                 engineering",
}

@Proceedings{Barbara:2003:PTS,
  editor =       "Daniel Barbar{\'a}",
  booktitle =    "{Proceedings of the Third SIAM International
                 Conference on Data Mining: [San Francisco, CA, May
                 1--3, 2003]}",
  title =        "{Proceedings of the Third SIAM International
                 Conference on Data Mining: [San Francisco, CA, May
                 1--3, 2003]}",
  publisher =    pub-SIAM,
  address =      pub-SIAM:adr,
  pages =        "xiii + 347",
  year =         "2003",
  ISBN =         "0-89871-545-8",
  ISBN-13 =      "978-0-89871-545-3",
  LCCN =         "QA76.9.D343",
  bibdate =      "Thu May 6 10:12:12 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  URL =          "http://www.gbv.de/dms/bowker/toc/9780898715453;
                 http://www.zentralblatt-math.org/zmath/en/search/?an=1076.68524",
  acknowledgement = ack-nhfb,
}

@Proceedings{Chick:2003:PWS,
  editor =       "Stephen E. Chick and others",
  booktitle =    "{Proceedings of the 2003 Winter Simulation Conference:
                 Fairmont Hotel, New Orleans, LA, USA, December 7--10,
                 2003}",
  title =        "{Proceedings of the 2003 Winter Simulation Conference:
                 Fairmont Hotel, New Orleans, LA, USA, December 7--10,
                 2003}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "????",
  year =         "2003",
  ISBN =         "0-7803-8131-9",
  ISBN-13 =      "978-0-7803-8131-5",
  LCCN =         "QA76.5 .56 2003; QA76.9.C65 .W56 2003",
  bibdate =      "Thu May 6 13:44:36 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "ACM Order Number 578030. IEEE catalog number
                 03CH37499.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=8912",
  acknowledgement = ack-nhfb,
  meetingname =  "Winter Simulation Conference (2003: New Orleans, LA)",
  subject =      "digital computer simulation; congresses; simulation
                 methods",
}

@Proceedings{Helal:2003:SAI,
  editor =       "Abdelsalam A. Helal and others",
  booktitle =    "{2003 Symposium on Applications and the Internet:
                 proceedings: Orlando, Florida, January 27--31, 2003.
                 SAINT 2003}",
  title =        "{2003 Symposium on Applications and the Internet:
                 proceedings: Orlando, Florida, January 27--31, 2003.
                 SAINT 2003}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xiv + 430",
  year =         "2003",
  ISBN =         "0-7695-1872-9",
  ISBN-13 =      "978-0-7695-1872-5",
  LCCN =         "TK5105.875.I57 S95 2003",
  bibdate =      "Thu May 6 13:49:57 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=8426",
  acknowledgement = ack-nhfb,
  meetingname =  "Symposium on Applications and the Internet (3rd: 2003:
                 Orlando, Fla.)",
  subject =      "Internet; Congresses; Application software",
}

@Proceedings{Hencsey:2003:PTI,
  editor =       "Guszt{\'a}v Hencsey and Bebo White",
  booktitle =    "{Proceedings of the Twelfth International Conference
                 on World Wide Web: Budapest, Hungary, May 20--24,
                 2003}",
  title =        "{Proceedings of the Twelfth International Conference
                 on World Wide Web: Budapest, Hungary, May 20--24,
                 2003}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "xx + 752",
  year =         "2003",
  ISBN =         "1-58113-680-3",
  ISBN-13 =      "978-1-58113-680-7",
  LCCN =         "TK5105.888 .I58 2003",
  bibdate =      "Thu May 6 10:19:17 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 library.mit.edu:9909/mit01; z3950.gbv.de:20011/gvk",
  URL =          "http://portal.acm.org/toc.cfm?id=775152",
  acknowledgement = ack-nhfb,
  meetingname =  "International WWW Conference (12th: 2003: Budapest,
                 Hungary)",
  subject =      "World Wide Web; congresses; computer networks;
                 hypertext systems; Internet",
}

@Proceedings{IEEE:2003:IIS,
  editor =       "{IEEE}",
  booktitle =    "{12th IEEE International Symposium on High Performance
                 Distributed Computing: proceedings: 22--24 June, 2003,
                 Seattle, Washington}",
  title =        "{12th IEEE International Symposium on High Performance
                 Distributed Computing: proceedings: 22--24 June, 2003,
                 Seattle, Washington}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xii + 283",
  year =         "2003",
  ISBN =         "0-7695-1965-2",
  ISBN-13 =      "978-0-7695-1965-4",
  LCCN =         "QA76.9.D5 I157 2003",
  bibdate =      "Thu May 6 09:10:03 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number PR01965.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=8591",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE International Symposium on High Performance
                 Distributed Computing (12th: 2003: Seattle, Wash.)",
  subject =      "electronic data processing; distributed processing;
                 congresses",
}

@Proceedings{Liu:2003:ISW,
  editor =       "Jiming Liu and others",
  booktitle =    "{IEEE \slash WIC International Conference on Web
                 Intelligence, 2003, Halifax, NS, Canada. WI 2003.
                 Proceedings}",
  title =        "{IEEE \slash WIC International Conference on Web
                 Intelligence, 2003. Halifax, NS, Canada. WI 2003.
                 Proceedings}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxi + 730",
  year =         "2003",
  ISBN =         "0-7695-1932-6",
  ISBN-13 =      "978-0-7695-1932-6",
  LCCN =         "QA75.5 .I345 2003",
  bibdate =      "Thu May 6 09:07:36 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  URL =          "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8792",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE/WIC International Conference on Web Intelligence
                 (2003: Halifax, NS)",
  remark =       "Held jointly with IEEE/WIC International Conference on
                 Intelligent Agent Technology.",
  subject =      "electronic data processing; congresses; artificial
                 intelligence",
}

@Proceedings{Yang:2003:ICP,
  editor =       "Chu-Sing Yang and P. Sadayappan and others",
  booktitle =    "{2003 International Conference on Parallel Processing:
                 proceedings: 6--9 October, 2003, Kaohsiung, Taiwan}",
  title =        "{2003 International Conference on Parallel Processing:
                 proceedings: 6--9 October, 2003, Kaohsiung, Taiwan}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xvi + 647",
  year =         "2003",
  ISBN =         "0-7695-2017-0",
  ISBN-13 =      "978-0-7695-2017-9",
  LCCN =         "QA76.58 .I55 2003; QA76.6 .I548 2003",
  bibdate =      "Thu May 6 13:54:24 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number PR02017.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=8782",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Parallel Processing (32nd:
                 2003: Kao-hsiung, Taiwan)",
  subject =      "parallel processing (electronic computers);
                 congresses",
}

@Proceedings{Barolli:2004:ICA,
  editor =       "Leonard Barolli",
  booktitle =    "{18th International Conference on Advanced Information
                 Networking and Applications, 2004. AINA 2004, 29--31
                 March 2004, [Fukuoka, Japan. Proceedings]}",
  title =        "{18th International Conference on Advanced Information
                 Networking and Applications, 2004. AINA 2004, 29--31
                 March 2004, [Fukuoka, Japan. Proceedings]}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2004",
  ISBN =         "0-7695-2051-0",
  ISBN-13 =      "978-0-7695-2051-3",
  LCCN =         "TK5105.5 .I5616 2004",
  bibdate =      "Thu May 6 10:25:16 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE Computer Society Order Number P2051.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=9028",
  acknowledgement = ack-nhfb,
}

@Proceedings{Ghorbani:2004:PAC,
  editor =       "Ali A. Ghorbani",
  booktitle =    "{Proceedings of the 2nd Annual Communication Networks
                 and Services Research Conference, 19--21 May 2004,
                 Fredericton, New Brunswick}",
  title =        "{Proceedings of the 2nd Annual Communication Networks
                 and Services Research Conference, 19--21 May 2004,
                 Fredericton, New Brunswick}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xi + 364",
  year =         "2004",
  ISBN =         "0-7695-2096-0",
  ISBN-13 =      "978-0-7695-2096-4",
  LCCN =         "TK5101.A1",
  bibdate =      "Thu May 6 10:28:50 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=9316;
                 http://www.gbv.de/dms/bowker/toc/9780769520964",
  acknowledgement = ack-nhfb,
}

@Proceedings{IEEE:2004:SWI,
  editor =       "{IEEE}",
  booktitle =    "{SAINT 2004 Workshops: 2004 International Symposium on
                 Applications and the Internet: Workshops: proceedings:
                 26--30 January, 2004, Tokyo, Japan}",
  title =        "{SAINT 2004 Workshops: 2004 International Symposium on
                 Applications and the Internet: Workshops: proceedings:
                 26--30 January, 2004, Tokyo, Japan}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxiii + 409",
  year =         "2004",
  ISBN =         "0-7695-2050-2",
  ISBN-13 =      "978-0-7695-2050-6",
  LCCN =         "TK5105.875.I57 S95 2004",
  bibdate =      "Thu May 6 09:13:55 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number PR02050.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=8957",
  acknowledgement = ack-nhfb,
  meetingname =  "Symposium on Applications and the Internet (4th: 2004:
                 Tokyo, Japan)",
  subject =      "Internet; congresses",
}

@Proceedings{Leonardi:2004:AMW,
  editor =       "S. (Stefano) Leonardi",
  booktitle =    "{Algorithms and models for the web-graph: third
                 international workshop, WAW 2004, Rome, Italy, October
                 16, 2004: proceedings}",
  title =        "{Algorithms and models for the web-graph: third
                 international workshop, WAW 2004, Rome, Italy, October
                 16, 2004: proceedings}",
  volume =       "3243",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "viii + 187",
  year =         "2004",
  ISBN =         "3-540-23427-6",
  ISBN-13 =      "978-3-540-23427-2",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA76.9.A43 W695 2004",
  bibdate =      "Thu May 6 12:25:46 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  series =       ser-LNCS,
  URL =          "http://library.ust.hk/cgi/db/springer.scr?0302-9743/3243;
                 http://www.loc.gov/catdir/enhancements/fy0823/2004113291-d.html",
  acknowledgement = ack-nhfb,
  meetingname =  "Workshop on Algorithms and Models for the Web-Graph
                 (3rd: 2004: Rome, Italy)",
  remark =       "This volume contains papers presented at the 3rd
                 Workshop on Algorithms and Models for the Web-Graph
                 (WAW 2004) in conjunction with the 45th Annual IEEE
                 Symposium on Foundations of Computer Science (FOCS
                 2004).",
  subject =      "computer algorithms; congresses; data mining",
}

@Proceedings{Zhong:2004:IWS,
  editor =       "Ning Zhong and others",
  booktitle =    "{IEEE\slash WIC \slash ACM International Conference on
                 Web Intelligence (WI 2004): Beijing, China, September
                 20--24, 2004: proceedings}",
  title =        "{IEEE\slash WIC \slash ACM International Conference on
                 Web Intelligence (WI 2004): Beijing, China, September
                 20--24, 2004: proceedings}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxviii + 811",
  year =         "2004",
  ISBN =         "0-7695-2100-2",
  ISBN-13 =      "978-0-7695-2100-8",
  LCCN =         "QA75.5 .I345 2004 .I429 2004; Q334 .I429 2004",
  bibdate =      "Thu May 6 14:08:43 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2100.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=9689",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE/WIC/ACM International Conference on Web
                 Intelligence (2004: Beijing, China)",
  subject =      "Artificial intelligence; Congresses",
}

@Proceedings{Barolli:2005:ICP,
  editor =       "Leonard Barolli and Jianhua Ma and Laurence Tianruo
                 Yang",
  booktitle =    "{11th International Conference on Parallel and
                 Distributed Systems, July 20--22, 2005, Fukuoka
                 Institute of Technology (FIT), Fukuoka, Japan:
                 proceedings}",
  title =        "{11th International Conference on Parallel and
                 Distributed Systems, July 20--22, 2005, Fukuoka
                 Institute of Technology (FIT), Fukuoka, Japan:
                 proceedings}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2005",
  ISBN =         "0-7695-2281-5",
  ISBN-13 =      "978-0-7695-2281-4",
  LCCN =         "QA76.58 .I576 2005",
  bibdate =      "Thu May 6 09:16:01 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2281.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10248",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Parallel and Distributed
                 Systems (11th: 2005: Fukuoka-shi, Japan)",
  subject =      "electronic data processing; distributed processing;
                 congresses; parallel processing (electronic
                 computers)",
}

@Proceedings{Han:2005:FII,
  editor =       "Jiawei Han and others",
  booktitle =    "{Fifth IEEE International Conference on Data Mining.
                 ICDM 2005. 27--30 November 2005, Houston, Texas.
                 Proceedings}",
  title =        "{Fifth IEEE International Conference on Data Mining.
                 ICDM 2005. 27--30 November 2005, Houston, Texas.
                 Proceedings}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxvii + 846",
  year =         "2005",
  ISBN =         "0-7695-2278-5",
  ISBN-13 =      "978-0-7695-2278-4",
  ISSN =         "1550-4786",
  LCCN =         "QA76.9.D343 I133 2005",
  bibdate =      "Thu May 6 15:15:08 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE Computer Society Order Number P2278.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10470",
  acknowledgement = ack-nhfb,
}

@Proceedings{He:2005:TIC,
  editor =       "Xiangjian He and others",
  booktitle =    "{Third International Conference on Information
                 Technology and Applications (ICITA 2005): 4--7 July
                 2005, Sydney, Australia: proceedings}",
  title =        "{Third International Conference on Information
                 Technology and Applications (ICITA 2005): 4--7 July
                 2005, Sydney, Australia: proceedings}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2005",
  ISBN =         "0-7695-2316-1",
  ISBN-13 =      "978-0-7695-2316-3",
  LCCN =         "T58.5 .I545 2005",
  bibdate =      "Thu May 6 09:17:57 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society Order Number P2316.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=9966",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Information Technology and
                 Applications (3rd: 2005: Sydney, NSW)",
  subject =      "information technology; congresses; application
                 software",
}

@Proceedings{IEEE:2005:EIC,
  editor =       "{IEEE}",
  booktitle =    "{14th Euromicro International Conference on Parallel,
                 Distributed, and Network-Based Processing: proceedings:
                 15--17 February 2006: Montb\'eliard-Sochaux, France}",
  title =        "{14th Euromicro International Conference on Parallel,
                 Distributed, and Network-Based Processing: proceedings:
                 15--17 February 2006: Montb\'eliard-Sochaux, France}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xviii + 474",
  year =         "2005",
  ISBN =         "0-7695-2513-X",
  ISBN-13 =      "978-0-7695-2513-6",
  LCCN =         "QA76.58 .E95 2006",
  bibdate =      "Thu May 6 10:35:20 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  note =         "IEEE Computer Society order number P2513.",
  acknowledgement = ack-nhfb,
  meetingname =  "Euromicro Conference on Parallel, Distributed, and
                 Network-based Processing (14th: 2006:
                 Montb\'eliard-Sochaux, France)",
  subject =      "parallel programming (computer science); congresses;
                 electronic data processing; distributed processing",
}

@Proceedings{IEEE:2005:ICD,
  editor =       "{IEEE}",
  booktitle =    "{25th International Conference on Distributed
                 Computing Systems: proceedings: 6--10 June, 2005,
                 Columbus, Ohio, USA}",
  title =        "{25th International Conference on Distributed
                 Computing Systems: proceedings: 6--10 June, 2005,
                 Columbus, Ohio, USA}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xviii + 827",
  year =         "2005",
  ISBN =         "0-7695-2331-5",
  ISBN-13 =      "978-0-7695-2331-6",
  LCCN =         "QA76.9.D5 I57 2005",
  bibdate =      "Fri May 7 22:34:07 MDT 2010",
  bibsource =    "alpha.lib.uwo.ca:210/INNOPAC;
                 fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "IEEE Computer Society order number P2331.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=9816",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Distributed Computing
                 Systems (25th: 2005: Columbus, Ohio)",
  subject =      "electronic data processing; distributed processing;
                 congresses; computer networks",
}

@Proceedings{IEEE:2005:PII,
  editor =       "{IEEE}",
  booktitle =    "{Proceedings of 2005 IEEE International Conference on
                 Natural Language Processing and Knowledge Engineering:
                 (IEEE NLP-KE'05): October 30--November 1, 2005, Wuhan,
                 China}",
  title =        "{Proceedings of 2005 IEEE International Conference on
                 Natural Language Processing and Knowledge Engineering:
                 (IEEE NLP-KE'05): October 30--November 1, 2005, Wuhan,
                 China}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "10 + 843 + 11",
  year =         "2005",
  ISBN =         "0-7803-9361-9",
  ISBN-13 =      "978-0-7803-9361-5",
  LCCN =         "QA76.9.N38 I563 2005",
  bibdate =      "Fri May 7 19:25:20 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  note =         "IEEE Catalog Number: 05EX1156.",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Natural Language
                 Processing and Knowledge Engineering (2005: Wuhan,
                 China)",
}

@Proceedings{Meng:2005:IIC,
  editor =       "Max Meng and others",
  booktitle =    "{IEEE International Conference on Information
                 Acquisition, 2005. 27 June--3 July 2005, [the Chinese
                 University of Hong Kong and the University of Macau]}",
  title =        "{IEEE International Conference on Information
                 Acquisition, 2005. 27 June--3 July 2005, [the Chinese
                 University of Hong Kong and the University of Macau]}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "609",
  year =         "2005",
  ISBN =         "0-7803-9303-1",
  ISBN-13 =      "978-0-7803-9303-5",
  LCCN =         "TA165 .I57 2005",
  bibdate =      "Thu May 6 15:36:25 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE catalog number 05EX1134.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10894",
  acknowledgement = ack-nhfb,
}

@Proceedings{Nesi:2005:FIC,
  editor =       "Paolo Nesi and Kia Ng and Jaime Delgado and others",
  booktitle =    "{First International Conference on Automated
                 Production of Cross Media Content for Multi-channel
                 Distribution: proceedings: Florence, Italy, 30
                 November--2 December 2005}",
  title =        "{First International Conference on Automated
                 Production of Cross Media Content for Multi-channel
                 Distribution: proceedings: Florence, Italy, 30
                 November--2 December 2005}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xi + 304",
  year =         "2005",
  ISBN =         "0-7695-2348-X",
  ISBN-13 =      "978-0-7695-2348-4",
  LCCN =         "QA76.575 .I633 2005",
  bibdate =      "Thu May 6 15:56:58 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number: P2348.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10605",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Automated Production of
                 Cross Media Content for Multi-channel Distribution
                 (1st: 2005: Florence, Italy)",
  subject =      "multimedia systems; congresses; music; data
                 processing",
}

@Proceedings{Skowron:2005:PIW,
  editor =       "Andrzej Skowron",
  booktitle =    "{Proceedings of the 2005 IEEE\slash WIC\slash ACM
                 International Conference on Web Intelligence, 2005.
                 September 19--22, 2005, Compi\`egne University of
                 Technology, France}",
  title =        "{Proceedings of the 2005 IEEE\slash WIC\slash ACM
                 International Conference on Web Intelligence, 2005.
                 September 19--22, 2005, Compi\`egne University of
                 Technology, France}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxxii + 819",
  year =         "2005",
  ISBN =         "0-7695-2415-X",
  ISBN-13 =      "978-0-7695-2415-3",
  LCCN =         "TK5105.888 .I37 2005",
  bibdate =      "Thu May 6 16:19:56 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE Computer Society Order Number P2415.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10179;
                 http://www.gbv.de/dms/bowker/toc/9780769524153",
  acknowledgement = ack-nhfb,
}

@Proceedings{Barga:2006:IPI,
  editor =       "Roger S. Barga and Xiaofang Zhou",
  booktitle =    "{ICDE '06: proceedings: 22nd International Conference
                 on Data Engineering workshops: 3--7 April, 2006,
                 Atlanta, Georgia}",
  title =        "{ICDE '06: proceedings: 22nd International Conference
                 on Data Engineering workshops: 3--7 April, 2006,
                 Atlanta, Georgia}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2006",
  ISBN =         "0-7695-2571-7",
  ISBN-13 =      "978-0-7695-2571-6",
  LCCN =         "QA76.9.D3 I5582 2006",
  bibdate =      "Thu May 6 14:28:27 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10810",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Data Engineering (22nd:
                 2006: Atlanta, Ga.). Workshops",
  subject =      "database management; congresses; electronic data
                 processing",
}

@Proceedings{Clifton:2006:SIC,
  editor =       "Christopher Wade Clifton and others",
  booktitle =    "{Sixth International Conference on Data Mining: ICDM
                 2006: proceedings: 18--22 December, 2006, Hong Kong}",
  title =        "{Sixth International Conference on Data Mining: ICDM
                 2006: proceedings: 18--22 December, 2006, Hong Kong}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxviii + 1221",
  year =         "2006",
  ISBN =         "0-7695-2702-7",
  ISBN-13 =      "978-0-7695-2702-4",
  LCCN =         "QA76.9.D343 I133 2006",
  bibdate =      "Thu May 6 15:43:32 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2701.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4053012",
  acknowledgement = ack-nhfb,
  meetingname =  "ICDM Workshops (2006: Hong Kong, China)",
  subject =      "data mining; congresses",
}

@Proceedings{Feng:2006:IMM,
  editor =       "Huamin Feng and Shiqiang Yang and Yueting Zhuang",
  booktitle =    "{The 12th International MuIti-Media ModelIing
                 Conference proceedings: MMM2006, 4--6 January 2006,
                 Beijing, China}",
  title =        "{The 12th International MuIti-Media ModelIing
                 Conference proceedings: MMM2006, 4--6 January 2006,
                 Beijing, China}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "489 + 3",
  year =         "2006",
  ISBN =         "1-4244-0028-7",
  ISBN-13 =      "978-1-4244-0028-7",
  LCCN =         "QA76.575 .I6526 2006",
  bibdate =      "Thu May 6 14:44:51 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE catalog number: 06EX1249.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10988",
  acknowledgement = ack-nhfb,
  meetingname =  "International Multimedia Modelling Conference (12th:
                 2006: Beijing, China)",
  subject =      "Multimedia systems; Congresses; Computer graphics",
}

@Proceedings{IEEE:2006:AAC,
  editor =       "{IEEE}",
  booktitle =    "{ADCOM 2006: autonomic computing: proceedings: 2006
                 (14th) International Conference on Advanced Computing
                 and Communications: December 20--23, 2006, National
                 Institute of Technology Karnataka, Surathkal, India}",
  title =        "{ADCOM 2006: autonomic computing: proceedings: 2006
                 (14th) International Conference on Advanced Computing
                 and Communications: December 20--23, 2006, National
                 Institute of Technology Karnataka, Surathkal, India}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  year =         "2006",
  ISBN =         "1-4244-0716-8",
  ISBN-13 =      "978-1-4244-0716-3",
  LCCN =         "QA75.5 .I5745 2006eb",
  bibdate =      "Thu May 6 17:36:18 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.lib.umich.edu:210/miu01_pub",
  note =         "IEEE catalog number 06EX1537C.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4289832",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Advanced Computing and
                 Communications (14th: 2006: Karnataka, India)",
  subject =      "computers; congresses; computer systems; electronic
                 data processing; autonomic computing",
}

@Proceedings{IEEE:2006:AIS,
  editor =       "{IEEE}",
  booktitle =    "{47th Annual IEEE Symposium on Foundations of Computer
                 Science: FOCS 2006: 21--24 October, 2006, Berkeley,
                 California}",
  title =        "{47th Annual IEEE Symposium on Foundations of Computer
                 Science: FOCS 2006: 21--24 October, 2006, Berkeley,
                 California}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xiv + 750",
  year =         "2006",
  ISBN =         "0-7695-2720-5, 0-7695-2362-5",
  ISBN-13 =      "978-0-7695-2720-8, 978-0-7695-2362-0",
  LCCN =         "QA76 .S974 2006",
  bibdate =      "Thu May 6 08:30:22 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2720.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4031329",
  acknowledgement = ack-nhfb,
  meetingname =  "Symposium on Foundations of Computer Science (47th:
                 2006: Berkeley, California)",
  subject =      "electronic data processing; congresses; machine
                 theory",
}

@Proceedings{IEEE:2006:ASE,
  editor =       "{IEEE}",
  booktitle =    "{2006 Australian Software Engineering Conference:
                 ASWEC 2006: 18--21 April, 2006, Sydney, Australia}",
  title =        "{2006 Australian Software Engineering Conference:
                 ASWEC 2006: 18--21 April, 2006, Sydney, Australia}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xv + 422",
  year =         "2006",
  ISBN =         "0-7695-2551-2",
  ISBN-13 =      "978-0-7695-2551-8",
  LCCN =         "QA76.758 .A89 2006",
  bibdate =      "Thu May 6 15:15:52 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2251.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10753",
  acknowledgement = ack-nhfb,
  meetingname =  "Australian Software Engineering Conference (2006:
                 Sydney, NSW)",
  subject =      "Software engineering; Congresses",
}

@Proceedings{IEEE:2006:CIT,
  editor =       "{IEEE}",
  booktitle =    "{Communications and Information Technologies, 2006.
                 ISCIT '06, International Symposium on: Oct. 18
                 2006--Sept. 20 2006, [Bangkok, Thailand]}",
  title =        "{Communications and Information Technologies, 2006.
                 ISCIT '06, International Symposium on: Oct. 18
                 2006--Sept. 20 2006, [Bangkok, Thailand]}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "936",
  year =         "2006",
  ISBN =         "0-7803-9741-X",
  ISBN-13 =      "978-0-7803-9741-5",
  LCCN =         "TK5105; TK5105eb; Internet",
  bibdate =      "Thu May 6 14:38:56 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4141327",
  acknowledgement = ack-nhfb,
  meetingname =  "International Symposium on Communications and
                 Information Technologies (6th: 2006: Bangkok,
                 Thailand)",
  subject =      "data transmission systems; congresses; information
                 technology; Internet; signal processing",
}

@Proceedings{IEEE:2006:ICC,
  editor =       "{IEEE}",
  booktitle =    "{9th International Conference on Control, Automation,
                 Robotics and Vision, 2006. ICARCV '06. 5--8 December
                 2006, Singapore}",
  title =        "{9th International Conference on Control, Automation,
                 Robotics and Vision, 2006. ICARCV '06. 5--8 December
                 2006, Singapore}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxii + 2428",
  year =         "2006",
  ISBN =         "1-4244-0341-3",
  ISBN-13 =      "978-1-4244-0341-7",
  LCCN =         "TJ212.2 .I5474 2006",
  bibdate =      "Thu May 6 15:06:01 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE catalog number 06EX1361.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4149990",
  acknowledgement = ack-nhfb,
}

@Proceedings{IEEE:2006:IIM,
  editor =       "{IEEE}",
  booktitle =    "{ICN 2006, ICONS 2006, MCL 2006: proceedings, Morne,
                 Mauritius, 23--29 April, 2006}",
  title =        "{ICN 2006, ICONS 2006, MCL 2006: proceedings, Morne,
                 Mauritius, 23--29 April, 2006}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  year =         "2006",
  ISBN =         "0-7695-2552-0",
  ISBN-13 =      "978-0-7695-2552-5",
  LCCN =         "See",
  bibdate =      "Thu May 6 17:38:14 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.lib.umich.edu:210/miu01_pub",
  note =         "IEEE Computer Society order number P2552.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10841",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Networking (5th: 2006:
                 Morne, Mauritius)",
  subject =      "computer networks; congresses; computer systems;
                 mobile communication systems in education",
}

@Proceedings{IEEE:2006:IJC,
  editor =       "{IEEE}",
  booktitle =    "{2006 International Joint Conference on Neural
                 Networks, Sheraton Vancouver Wall Centre Hotel,
                 Vancouver, BC, Canada, July 16--21, 2006}",
  title =        "{2006 International Joint Conference on Neural
                 Networks, Sheraton Vancouver Wall Centre Hotel,
                 Vancouver, BC, Canada, July 16--21, 2006}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  year =         "2006",
  ISBN =         "0-7803-9490-9",
  ISBN-13 =      "978-0-7803-9490-2",
  LCCN =         "QA76.87 2006",
  bibdate =      "Thu May 6 16:16:00 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=11216",
  acknowledgement = ack-nhfb,
  meetingname =  "International Joint Conference on Neural Networks
                 (2006: Vancouver, BC)",
}

@Proceedings{IEEE:2006:ISL,
  editor =       "{IEEE}",
  booktitle =    "{2006 IEEE Spoken Language Technology Workshop: Palm
                 Beach, Aruba, 10--13 December 2006}",
  title =        "{2006 IEEE Spoken Language Technology Workshop: Palm
                 Beach, Aruba, 10--13 December 2006}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xiii + 261 + 2",
  year =         "2006",
  ISBN =         "1-4244-0872-5 (softbound edition)",
  ISBN-13 =      "978-1-4244-0872-6 (softbound edition)",
  LCCN =         "TK7895.S65",
  bibdate =      "Thu May 6 16:42:21 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE catalog number 06EX1646.",
  acknowledgement = ack-nhfb,
  subject =      "automatic speech recognition; congresses; natural
                 language processing (computer science); computational
                 linguistics",
}

@Proceedings{IEEE:2006:ISP,
  editor =       "{IEEE}",
  booktitle =    "{International Symposium on Parallel Computing in
                 Electrical Engineering, 2006. PARELEC 2006. 13--17
                 September 2006, Bialystok, Poland. Proceedings}",
  title =        "{International Symposium on Parallel Computing in
                 Electrical Engineering, 2006. PARELEC 2006. 13--17
                 September 2006, Bialystok, Poland. Proceedings}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xvii + 478",
  year =         "2006",
  ISBN =         "0-7695-2554-7",
  ISBN-13 =      "978-0-7695-2554-9",
  LCCN =         "QA76.58. I578 2006",
  bibdate =      "Thu May 6 15:57:44 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE Computer Society order number P2554.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=11156",
  acknowledgement = ack-nhfb,
}

@Proceedings{IEEE:2006:SIC,
  editor =       "{IEEE}",
  booktitle =    "{Sixth International Conference on Advanced Learning
                 Technologies, 2006. ICALT 2006. 5--7 July 2006,
                 Kerkrade, The Netherlands. Proceedings}",
  title =        "{Sixth International Conference on Advanced Learning
                 Technologies, 2006. ICALT 2006 5--7 July 2006,
                 Kerkrade, The Netherlands. Proceedings}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxxi + 1215",
  year =         "2006",
  ISBN =         "1-4244-3075-5",
  ISBN-13 =      "978-1-4244-3075-8",
  LCCN =         "????",
  bibdate =      "Thu May 6 16:06:29 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10997",
  acknowledgement = ack-nhfb,
  remark =       "Parallel als Druckausg. erschienen.",
}

@Proceedings{IEEE:2006:WSW,
  editor =       "{IEEE}",
  booktitle =    "{WCICA 2006: Six World Congress on Intelligent Control
                 and Automation: June 21--23, 2006, Dalian, China}",
  title =        "{WCICA 2006: Six World Congress on Intelligent Control
                 and Automation: June 21--23, 2006, Dalian, China}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  year =         "2006",
  ISBN =         "1-4244-0332-4",
  ISBN-13 =      "978-1-4244-0332-5",
  LCCN =         "TJ217.5 2006",
  bibdate =      "Thu May 6 09:24:59 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE catalog number 06EX1358C.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=11210",
  acknowledgement = ack-nhfb,
  meetingname =  "World Congress on Intelligent Control and Automation
                 (6th: 2006: Dalian Shi, China)",
  subject =      "intelligent control systems; congresses; automation",
}

@Proceedings{Jeong:2006:SII,
  editor =       "Chang-Sung Jeong and others",
  booktitle =    "{Sixth IEEE International Conference on Computer and
                 Information Technology: CIT 2006. 20--22 September
                 2006, Korea University, Seoul, Korea}",
  title =        "{Sixth IEEE International Conference on Computer and
                 Information Technology: CIT 2006. 20--22 September
                 2006, Korea University, Seoul, Korea}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxiv + 276",
  year =         "2006",
  ISBN =         "0-7695-2687-X",
  ISBN-13 =      "978-0-7695-2687-4",
  LCCN =         "T58.5 .I5662 2006",
  bibdate =      "Thu May 6 16:16:17 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE Computer Society order number E2687.",
  URL =          "http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=4019822",
  acknowledgement = ack-nhfb,
}

@Proceedings{Nishida:2006:IWA,
  editor =       "T. (Toyoaki) Nishida and others",
  booktitle =    "{2006 IEEE\slash WIC\slash ACM International
                 Conference on Web Intelligence: (WI 2006 main
                 conference proceedings) (WI '06): proceedings: 18--22
                 December 2006, Hong Kong, China}",
  title =        "{2006 IEEE\slash WIC\slash ACM International
                 Conference on Web Intelligence: (WI 2006 main
                 conference proceedings) (WI '06): proceedings: 18--22
                 December 2006, Hong Kong, China}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxviii + 1085",
  year =         "2006",
  ISBN =         "0-7695-2747-7",
  ISBN-13 =      "978-0-7695-2747-5",
  LCCN =         "TK5105.888 2006",
  bibdate =      "Thu May 6 16:02:18 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 library.mit.edu:9909/mit01",
  note =         "IEEE Computer Society order number P2747.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4061321",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE\slash WIC\slash ACM International Conference on
                 Web Intelligence (6th: 2006: Hong Kong, China)",
  subject =      "World Wide Web; congresses; artificial intelligence;
                 data mining",
}

@Proceedings{Perner:2006:ADM,
  editor =       "Petra Perner",
  booktitle =    "{Advances in data mining: applications in medicine,
                 web mining, marketing, image and signal mining: 6th
                 Industrial Conference on Data Mining, ICDM 2006,
                 Leipzig, Germany, July 14--15, 2006: proceedings}",
  title =        "{Advances in data mining: applications in medicine,
                 web mining, marketing, image and signal mining: 6th
                 Industrial Conference on Data Mining, ICDM 2006,
                 Leipzig, Germany, July 14--15, 2006: proceedings}",
  volume =       "4065",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "xi + 592",
  year =         "2006",
  ISBN =         "3-540-36036-0",
  ISBN-13 =      "978-3-540-36036-0",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA76.9.D343",
  bibdate =      "Thu May 06 17:11:23 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 prodorbis.library.yale.edu:7090/voyager",
  series =       ser-LNCS,
  acknowledgement = ack-nhfb,
  meetingname =  "Industrial Conference on Data Mining (6th: 2006:
                 Leipzig, Germany)",
  subject =      "Data mining; Congresses",
}

@Proceedings{Turner:2006:SII,
  editor =       "Stephen John Turner and Bu Sung Lee and Wientong
                 Cai",
  booktitle =    "{Sixth IEEE International Symposium on Cluster
                 Computing and the Grid workshops, 2006: CCGrid 06.
                 16--19 May 2006, Singapore}",
  title =        "{Sixth IEEE International Symposium on Cluster
                 Computing and the Grid workshops, 2006: CCGrid 06.
                 16--19 May 2006, Singapore}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxiii + 662",
  year =         "2006",
  ISBN =         "0-7695-2585-7",
  ISBN-13 =      "978-0-7695-2585-3",
  LCCN =         "QA76.9.C58 I133 2006",
  bibdate =      "Thu May 6 14:57:13 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE Computer Society Order Number P2585.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10857",
  acknowledgement = ack-nhfb,
}

@Proceedings{Wombacher:2006:JCC,
  editor =       "Andreas Wombacher and Christian Huemer and Markus
                 Stolze and others",
  booktitle =    "{Joint Conference: CEC\slash EEE 2006 Joint
                 Conference: 8th IEEE International Conference on
                 E-Commerce and Technology (CEC 2006): 3rd IEEE
                 International Conference on Enterprise Computing,
                 E-Commerce and E-Services (EEE 2006): 3rd IEEE
                 International Workshop on Mobile Commerce and Wireless
                 Services (WMCS 2006): Joint Workshop: 2nd International
                 Workshop on Business Service Networks (BSN 2006): 2nd
                 International Workshop on Service Oriented Solutions
                 for Cooperative Organizations (SoS4CO ): June 26--29,
                 2006, San Francisco, California}",
  title =        "{Joint Conference: CEC\slash EEE 2006 Joint
                 Conference: 8th IEEE International Conference on
                 E-Commerce and Technology (CEC 2006): 3rd IEEE
                 International Conference on Enterprise Computing,
                 E-Commerce and E-Services (EEE 2006): 3rd IEEE
                 International Workshop on Mobile Commerce and Wireless
                 Services (WMCS 2006): Joint Workshop: 2nd International
                 Workshop on Business Service Networks (BSN 2006): 2nd
                 International Workshop on Service Oriented Solutions
                 for Cooperative Organizations (SoS4CO ): June 26--29,
                 2006, San Francisco, California}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxiv + 588",
  year =         "2006",
  ISBN =         "0-7695-2511-3",
  ISBN-13 =      "978-0-7695-2511-2",
  LCCN =         "HF5548.32 .I57 2006",
  bibdate =      "Thu May 6 15:53:12 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10920",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE International Conference on E-commerce
                 Technology. Workshops and Conferences (8th: 2006: San
                 Francisco, Calif.)",
  subject =      "electronic commerce; congresses; organizational
                 change",
}

@Proceedings{Zhang:2006:IIC,
  editor =       "Yan-Qing Zhang and Tsau Y. Lin",
  booktitle =    "{2006 IEEE International Conference on Granular
                 Computing: Atlanta, USA, May 10--12, 2006}",
  title =        "{2006 IEEE International Conference on Granular
                 Computing: Atlanta, USA, May 10--12, 2006}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  year =         "2006",
  ISBN =         "1-4244-0134-8",
  ISBN-13 =      "978-1-4244-0134-5",
  LCCN =         "QA76.9.S63 2006",
  bibdate =      "Thu May 6 09:54:29 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE catalog number 06EX1286.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10898",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE International Conference on Granular Computing
                 (2006: Atlanta, Ga.)",
  subject =      "granular computing; congresses",
}

@Proceedings{Bonato:2007:AMW,
  editor =       "Anthony Bonato and Fan R. K. Chung",
  booktitle =    "{Algorithms and models for the web-graph: 5th
                 international workshop, WAW 2007, San Diego, CA, USA,
                 December 11-12, 2007: proceedings}",
  title =        "{Algorithms and models for the web-graph: 5th
                 international workshop, WAW 2007, San Diego, CA, USA,
                 December 11-12, 2007: proceedings}",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "x + 216",
  year =         "2007",
  ISBN =         "3-540-77003-8 (softcover)",
  ISBN-13 =      "978-3-540-77003-9 (softcover)",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA76.9.A43 W39 2007; QA76.9.A43 W428 2007; QA76.9.A43
                 W428 2007; Internet; QA76.9.A43 W63 2007",
  bibdate =      "Thu May 6 08:22:51 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  series =       ser-LNCS,
  acknowledgement = ack-nhfb,
  meetingname =  "WAW 2007 (2007: San Diego, Calif.)",
  subject =      "computer algorithms; congresses; data mining",
}

@Proceedings{Cai:2007:TAM,
  editor =       "Jin-yi Cai and S. B. (S. Barry) Cooper and Hong Zhu",
  booktitle =    "{Theory and applications of models of computation: 4th
                 international conference, TAMC 2007, Shanghai, China,
                 May 22--25, 2007: proceedings}",
  title =        "{Theory and applications of models of computation: 4th
                 international conference, TAMC 2007, Shanghai, China,
                 May 22--25, 2007: proceedings}",
  volume =       "4484",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "xiii + 772",
  year =         "2007",
  ISBN =         "3-540-72503-2 (softcover)",
  ISBN-13 =      "978-3-540-72503-9 (softcover)",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA267.7 .T36 2007",
  bibdate =      "Thu May 6 10:38:25 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  series =       ser-LNCS,
  URL =          "http://www.springerlink.com/openurl.asp?genre=issue&issn=0302-9743&volume=4484",
  acknowledgement = ack-nhfb,
  meetingname =  "TAMC 2007 (2007: Shanghai, China)",
  subject =      "Computational complexity; Congresses; Computable
                 functions",
}

@Proceedings{Dini:2007:SIC,
  editor =       "Oana Dini and others",
  booktitle =    "{Second International Conference on Systems and
                 Networks Communications: ICSNC 2007, 25--31 August
                 2007. HPC-Bio 2007: the First International Workshop on
                 High Performance Computing Applied to Medical Data and
                 Bioinformatics}",
  title =        "{Second International Conference on Systems and
                 Networks Communications: ICSNC 2007, 25--31 August
                 2007. HPC-Bio 2007: the First International Workshop on
                 High Performance Computing Applied to Medical Data and
                 Bioinformatics}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2007",
  ISBN =         "0-7695-2938-0",
  ISBN-13 =      "978-0-7695-2938-7",
  LCCN =         "TK5105.5 I5727 2006e",
  bibdate =      "Thu May 6 15:27:18 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2938.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4299965",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Systems and Networks
                 Communications (2nd: 2007: Riviera, France)",
  subject =      "computer networks; congresses; wireless communication
                 systems",
}

@Proceedings{Feng:2007:EAI,
  editor =       "Wenying Feng and Feng Gao",
  booktitle =    "{Eighth ACIS International Conference on Software
                 Engineering, Artificial Intelligence, Networking, and
                 Parallel/Distributed Computing: SNPD 2007: [in
                 conjunction with 3rd [i.e. 8th] ACIS International
                 Workshop on Self-Assembling Networks: SAWN 2007]:
                 proceedings: 30 July--1 August 2007, Haier
                 International Training Center, Qingdao, China}",
  title =        "{Eighth ACIS International Conference on Software
                 Engineering, Artificial Intelligence, Networking, and
                 Parallel/Distributed Computing: SNPD 2007: [in
                 conjunction with 3rd [i.e. 8th] ACIS International
                 Workshop on Self-Assembling Networks: SAWN 2007]:
                 proceedings: 30 July--1 August 2007, Haier
                 International Training Center, Qingdao, China}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2007",
  ISBN =         "0-7695-2909-7",
  ISBN-13 =      "978-0-7695-2909-7",
  LCCN =         "QA76.758 .I573155",
  bibdate =      "Thu May 6 09:32:55 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2909.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4287452",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Software Engineering,
                 Artificial Intelligence, Networking, and
                 Parallel/Distributed Computing (8th: 2007: Qingdao Shi,
                 China)",
  subject =      "Software engineering; Congresses; Artificial
                 intelligence; Wireless communication systems",
}

@Proceedings{Hauswirth:2007:SII,
  editor =       "Manfred Hauswirth and others",
  booktitle =    "{Seventh IEEE International Conference on Peer-to-Peer
                 Computing, 2007. P2P 2007. 2--5 Sept. 2007, Galway,
                 Ireland. Proceedings}",
  title =        "{Seventh IEEE International Conference on Peer-to-Peer
                 Computing, 2007. P2P 2007. 2--5 Sept. 2007, Galway,
                 Ireland. Proceedings}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xiii + 253",
  year =         "2007",
  ISBN =         "0-7695-2986-0",
  ISBN-13 =      "978-0-7695-2986-8",
  LCCN =         "TK5105.525 .I58 2007",
  bibdate =      "Thu May 6 17:07:24 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE catalog nunber PR2986.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4343447",
  acknowledgement = ack-nhfb,
}

@Proceedings{IEEE:2007:BBE,
  editor =       "{IEEE}",
  booktitle =    "{Bioinformatics and Biomedical Engineering, 2007:
                 ICBBE 2007: The 1st International Conference, 6--8 July
                 2007, Wuhan, China}",
  title =        "{Bioinformatics and Biomedical Engineering, 2007:
                 ICBBE 2007: The 1st International Conference, 6--8 July
                 2007, Wuhan, China}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2007",
  ISBN =         "1-4244-1120-3",
  ISBN-13 =      "978-1-4244-1120-7",
  LCCN =         "QH324.2 2007",
  bibdate =      "Thu May 6 16:53:30 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE catalog number 07EX1744.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4272484",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Bioinformatics and
                 Biomedical Engineering (1st: 2007: Wuhan, China)",
  subject =      "bioinformatics; congresses; biomedical engineering",
}

@Proceedings{IEEE:2007:ICA,
  editor =       "{IEEE}",
  booktitle =    "{21st International Conference on Advanced Networking
                 and Applications Workshops/Symposia: proceedings:
                 Niagara Falls, Ontario, Canada: 21--23 May, 2007. AINA
                 '07}",
  title =        "{21st International Conference on Advanced Networking
                 and Applications Workshops/Symposia: proceedings:
                 Niagara Falls, Ontario, Canada: 21--23 May, 2007. AINA
                 '07}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2007",
  ISBN =         "0-7695-2847-3",
  ISBN-13 =      "978-0-7695-2847-2",
  LCCN =         "TK5105.5 2007; TK5105.5 .I5616 2007",
  bibdate =      "Thu May 6 09:31:02 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2847.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4221005",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Advanced Information
                 Networking and Applications (21st: 2007: Niagara Falls,
                 Ont.)",
  subject =      "Computer networks; Congresses; Information networks",
}

@Proceedings{IEEE:2007:ICC,
  editor =       "{IEEE}",
  booktitle =    "{2nd International Conference on Communication Systems
                 Software and Middleware, 2007. COMSWARE 2007. 7--12
                 January 2007, Bangalore, India}",
  title =        "{2nd International Conference on Communication Systems
                 Software and Middleware, 2007. COMSWARE 2007. 7--12
                 January 2007, Bangalore, India}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxvi + 994",
  year =         "2007",
  ISBN =         "1-4244-0614-5, 1-4244-0613-7",
  ISBN-13 =      "978-1-4244-0614-2, 978-1-4244-0613-5",
  LCCN =         "TK5101.A1 I479696 2007",
  bibdate =      "Thu May 6 16:48:08 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE catalog number 07EX1518.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4267954",
  acknowledgement = ack-nhfb,
}

@Proceedings{IEEE:2007:ICI,
  editor =       "{IEEE}",
  booktitle =    "{IPDPS 2007 California: International Parallel and
                 Distributed Processing Symposium: proceedings: 21st
                 International Parallel and Distributed Processing
                 Symposium: March 26--30, 2007, Long Beach, California,
                 USA}",
  title =        "{IPDPS 2007 California: International Parallel and
                 Distributed Processing Symposium: proceedings: 21st
                 International Parallel and Distributed Processing
                 Symposium: March 26--30, 2007, Long Beach, California,
                 USA}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxxv + 381",
  year =         "2007",
  ISBN =         "1-4244-0909-8 (paperback), 1-4244-0910-1 (CD-ROM)",
  ISBN-13 =      "978-1-4244-0909-9 (paperback), 978-1-4244-0910-5
                 (CD-ROM)",
  LCCN =         "QA76.58 .I586 2007",
  bibdate =      "Thu May 6 15:14:44 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  note =         "IEEE catalog number 07TH8938.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4203121",
  acknowledgement = ack-nhfb,
}

@Proceedings{IEEE:2007:ICN,
  editor =       "{IEEE}",
  booktitle =    "{International Conference on Natural Language
                 Processing and Knowledge Engineering, 2007. NLP-KE
                 2007. August 30--September 1, 2007, Beijing, China}",
  title =        "{International Conference on Natural Language
                 Processing and Knowledge Engineering, 2007. NLP-KE
                 2007. August 30--September 1, 2007, Beijing, China}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  year =         "2007",
  ISBN =         "1-4244-1611-6",
  ISBN-13 =      "978-1-4244-1611-0",
  LCCN =         "????",
  bibdate =      "Fri May 7 22:00:55 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 odin2.bib.sdu.dk:210/Horizon",
  acknowledgement = ack-nhfb,
}

@Proceedings{IEEE:2007:IWM,
  editor =       "{IEEE}",
  booktitle =    "{IEEE 9th Workshop on Multimedia Signal Processing,
                 2007. MMSP 2007. 1--3 October 2007, Chania, Crete,
                 Greece. Proceedings}",
  title =        "{IEEE 9th Workshop on Multimedia Signal Processing,
                 2007. MMSP 2007. 1--3 October 2007, Chania, Crete,
                 Greece. Proceedings}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xvi + 468",
  year =         "2007",
  ISBN =         "1-4244-1273-0",
  ISBN-13 =      "978-1-4244-1273-0",
  LCCN =         "????",
  bibdate =      "Thu May 6 15:02:35 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE catalog number 07EX1807.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4412795",
  acknowledgement = ack-nhfb,
}

@Proceedings{IEEE:2007:PTI,
  editor =       "{IEEE}",
  booktitle =    "{Proceedings, Third International Conference on
                 Semantics, Knowledge and Grid: SKG 2007: Xi'an, Shan
                 Xi, China, 29--31 October 2007}",
  title =        "{Proceedings, Third International Conference on
                 Semantics, Knowledge and Grid: SKG 2007: Xi'an, Shan
                 Xi, China, 29--31 October 2007}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xv + 632",
  year =         "2007",
  ISBN =         "0-7695-3007-9",
  ISBN-13 =      "978-0-7695-3007-9",
  LCCN =         "TK5105.88815 2007",
  bibdate =      "Thu May 6 15:40:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P3007.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4438492",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Semantics, Knowledge and
                 Grid (3rd: 2007: Xi'an Shi, China)",
  subject =      "semantic networks (information theory); congresses;
                 knowledge management; computational grids (computer
                 systems)",
}

@Proceedings{IEEE:2007:SICa,
  editor =       "{IEEE}",
  booktitle =    "{2007 Second International Conference on Bio-Inspired
                 Computing: Theories and Applications: (BIC-TA 2007);
                 Zhengzhou University of Light Industry, China, 14--17
                 September 2007}",
  title =        "{2007 Second International Conference on Bio-Inspired
                 Computing: Theories and Applications: (BIC-TA 2007);
                 Zhengzhou University of Light Industry, China, 14--17
                 September 2007}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "288",
  year =         "2007",
  ISBN =         "1-4244-4105-6",
  ISBN-13 =      "978-1-4244-4105-1",
  LCCN =         "QA76.87",
  bibdate =      "Thu May 6 14:48:32 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE catalog number CFP0701F-PRT.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4801442",
  acknowledgement = ack-nhfb,
}

@Proceedings{IEEE:2007:SICb,
  editor =       "{IEEE}",
  booktitle =    "{Second International Conference on Internet and Web
                 Applications and Services: ICIW 2007: May 13--19, 2007,
                 Morne, Maurititus: ENSYS 2007, the Second Workshop on
                 Entertainment Systems: P2PSA 2007, the Second
                 International Workshop on P2P Systems and Applications:
                 ONLINE 2007, the Second International Workshop on
                 Online Communications, Collaborative Systems, and
                 Social Network.: Internet and Web Applications and
                 Services, 2007, ICIW '07, Second International
                 Conference on}",
  title =        "{Second International Conference on Internet and Web
                 Applications and Services: ICIW 2007: May 13--19, 2007,
                 Morne, Maurititus: ENSYS 2007, the Second Workshop on
                 Entertainment Systems: P2PSA 2007, the Second
                 International Workshop on P2P Systems and Applications:
                 ONLINE 2007, the Second International Workshop on
                 Online Communications, Collaborative Systems, and
                 Social Network.: Internet and Web Applications and
                 Services, 2007, ICIW '07, Second International
                 Conference on}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "viii + 67",
  year =         "2007",
  ISBN =         "0-7695-2844-9",
  ISBN-13 =      "978-0-7695-2844-1",
  LCCN =         "TK5105.888 .I563 2007",
  bibdate =      "Thu May 6 16:29:56 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 library.mit.edu:9909/mit01",
  note =         "IEEE Computer Society order number E2844.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4222895",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Internet and Web
                 Applications and Services (2nd: 2007: Le Morne,
                 Mauritis)",
}

@Proceedings{Lei:2007:FPF,
  editor =       "Jingsheng Lei and Jian Yu and Shuigeng Zhou",
  booktitle =    "{FSKD 2007: proceedings: Fourth International
                 Conference on Fuzzy Systems and Knowledge Discovery:
                 24--27 August, 2007: Haikou, Hainan, China}",
  title =        "{FSKD 2007: proceedings: Fourth International
                 Conference on Fuzzy Systems and Knowledge Discovery:
                 24--27 August, 2007: Haikou, Hainan, China}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2007",
  ISBN =         "1-4244-1210-2, 0-7695-2874-0",
  ISBN-13 =      "978-1-4244-1210-5, 978-0-7695-2874-8",
  LCCN =         "TJ212.2 .I143 2007",
  bibdate =      "Thu May 6 10:42:54 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 library.mit.edu:9909/mit01",
  note =         "Four volumes. IEEE Computer Society order number
                 P2874.",
  URL =          "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4405869&isYear=2007
                 (vol. 1);
                 http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4406026&isYear=2007
                 (vol. 2);
                 http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4406182&isYear=2007
                 (vol. 3);
                 http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4406334&isYear=2007
                 (vol. 4)",
  acknowledgement = ack-nhfb,
  meetingname =  "FSKD 2007 (2007: Haikou Shi, China)",
  subject =      "Fuzzy systems; Congresses; Expert systems (Computer
                 science)",
}

@Proceedings{Lin:2007:PIW,
  editor =       "Tsau Y. Lin and others",
  booktitle =    "{Proceedings of the IEEE\slash WIC\slash ACM
                 International Conference on Web Intelligence (WI 2007):
                 November 2--5, 2007, Fremont Marriott Hotel, Silicon
                 Valley, USA}",
  title =        "{Proceedings of the IEEE\slash WIC\slash ACM
                 International Conference on Web Intelligence (WI 2007):
                 November 2--5, 2007, Fremont Marriott Hotel, Silicon
                 Valley, USA}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxxiv + 841",
  year =         "2007",
  ISBN =         "0-7695-3026-5",
  ISBN-13 =      "978-0-7695-3026-0",
  LCCN =         "QA76.76.I58; TK5105.888 .I35687 2007",
  bibdate =      "Thu May 6 14:36:01 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P3026.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4427043",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE/WIC/ACM International Conference on Web
                 Intelligence (2007: Silicon Valley, Calif.)",
  subject =      "intelligent agents (computer software); congresses;
                 artificial intelligence",
}

@Proceedings{Luzar-Stiffler:2007:PII,
  editor =       "Vesna Luzar-Stiffler",
  booktitle =    "{Proceedings of the ITI 2007, 29th International
                 Conference on Information Technology Interfaces, June
                 25--28, 2007, Cavtat\slash Dubrovnik, Croatia}",
  title =        "{Proceedings of the ITI 2007, 29th International
                 Conference on Information Technology Interfaces, June
                 25--28, 2007, Cavtat\slash Dubrovnik, Croatia}",
  publisher =    "SRCE University Computing Centre, University of
                 Zagreb",
  address =      "Zagreb, Croatia",
  pages =        "????",
  year =         "2007",
  ISBN =         "953-7138-10-0",
  ISBN-13 =      "978-953-7138-10-3",
  LCCN =         "????",
  bibdate =      "Thu May 6 15:17:46 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  acknowledgement = ack-nhfb,
  remark =       "IEEE catalog number 07EX1589C.",
}

@Proceedings{Miyazaki:2007:CPI,
  editor =       "T. (Toshiaki) Miyazaki and Incheon Paik and Daming Wei
                 and others",
  booktitle =    "{CIT 2007: proceedings: 7th IEEE International
                 Conference on Computer and Information Technology:
                 16--19 October, 2007, Aizu-Wakamatsu City, Fukushima,
                 Japan}",
  title =        "{CIT 2007: proceedings: 7th IEEE International
                 Conference on Computer and Information Technology:
                 16--19 October, 2007, Aizu-Wakamatsu City, Fukushima,
                 Japan}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxvi + 1131",
  year =         "2007",
  ISBN =         "0-7695-2983-6",
  ISBN-13 =      "978-0-7695-2983-7",
  LCCN =         "T58.5 .I56624 2007",
  bibdate =      "Thu May 6 15:49:36 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2983.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4385040",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE International Conference on Computer and
                 Information Technology (7th: 2007: Aizuwakamatsu-shi,
                 Japan)",
  subject =      "information technology; congresses; computers",
}

@Proceedings{Na:2007:IIC,
  editor =       "Yun Ji Na and others",
  booktitle =    "{ICCIT 2007: the 2007 International Conference on
                 Convergence Information Technology: Hydai Hotel,
                 Gyeongju, Korea, 21--23 November, 2007}",
  title =        "{ICCIT 2007: the 2007 International Conference on
                 Convergence Information Technology: Hydai Hotel,
                 Gyeongju, Korea, 21--23 November, 2007}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2007",
  ISBN =         "0-7695-3038-9",
  ISBN-13 =      "978-0-7695-3038-3",
  LCCN =         "????",
  bibdate =      "Thu May 6 16:41:46 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 library.mit.edu:9909/mit01",
  note =         "IEEE Computer Society order number E3038.",
  URL =          "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4420217&isYear=2007",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Convergence Information
                 Technology (2nd: 2007: Kyongju-si, Korea)",
  subject =      "computer science; congresses; information technology",
}

@Proceedings{Ock:2007:ASI,
  editor =       "CheolYoung Ock and JeongYong Byun and YuDe Bi",
  booktitle =    "{ALPIT 2007: Sixth International Conference on
                 Advanced Language Processing and Web Information
                 Technology: proceedings: August 22--24, 2007, Luoyang,
                 Henan, China}",
  title =        "{ALPIT 2007: Sixth International Conference on
                 Advanced Language Processing and Web Information
                 Technology: proceedings: August 22--24, 2007, Luoyang,
                 Henan, China}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xix + 615",
  year =         "2007",
  ISBN =         "0-7695-2930-5",
  ISBN-13 =      "978-0-7695-2930-1",
  LCCN =         "QA76.9.S88 2007",
  bibdate =      "Thu May 6 16:51:10 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4460594",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Advanced Language
                 Processing and Web Information Technology (6th: 2007:
                 Luoyang, Henan Sheng, China)",
  subject =      "system design; congresses; parallel processing
                 (electronic computers)",
}

@Proceedings{Ramakrishnan:2007:PSI,
  editor =       "Naren Ramakrishnan and others",
  booktitle =    "{Proceedings of the Seventh IEEE International
                 Conference on Data Mining: ICDM 2007: 28--31 October,
                 2007, Omaha, Nebraska}",
  title =        "{Proceedings of the Seventh IEEE International
                 Conference on Data Mining: ICDM 2007: 28--31 October,
                 2007, Omaha, Nebraska}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxviii + 771",
  year =         "2007",
  ISBN =         "0-7695-3018-4",
  ISBN-13 =      "978-0-7695-3018-5",
  LCCN =         "QA76.9.D343 I133 2007",
  bibdate =      "Thu May 6 16:11:47 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  note =         "IEEE Computer Society order number P3018.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4470209",
  acknowledgement = ack-nhfb,
}

@Proceedings{Tjoa:2007:DIC,
  editor =       "A. Min Tjoa and Roland R. Wagner",
  booktitle =    "{DEXA 2007: 18th International Conference on Database
                 and Expert Systems Applications: proceedings:
                 Regensburg, Germany, 3--7 September, 2007}",
  title =        "{DEXA 2007: 18th International Conference on Database
                 and Expert Systems Applications: proceedings:
                 Regensburg, Germany, 3--7 September, 2007}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xix + 863",
  year =         "2007",
  ISBN =         "0-7695-2932-1",
  ISBN-13 =      "978-0-7695-2932-5",
  LCCN =         "QA76.9.D3 2007",
  bibdate =      "Thu May 6 10:42:07 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2932.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4312838",
  acknowledgement = ack-nhfb,
  meetingname =  "International Workshop on Database and Expert Systems
                 Applications (18th: 2007: Regensberg, Germany)",
  subject =      "database management; congresses; expert systems
                 (computer science)",
}

@Proceedings{ACM:2008:PNA,
  editor =       "{ACM}",
  booktitle =    "{Proceedings of the Nineteenth Annual ACM-SIAM
                 Symposium on Discrete Algorithms: [San Francisco, CA,
                 January 20--22, 2008]}",
  title =        "{Proceedings of the Nineteenth Annual ACM-SIAM
                 Symposium on Discrete Algorithms: [San Francisco, CA,
                 January 20--22, 2008]}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "xvi + 1279",
  year =         "2008",
  ISBN =         "0-89871-647-0",
  ISBN-13 =      "978-0-89871-647-4",
  LCCN =         "QA76.9.A43 A34 2008",
  bibdate =      "Thu May 6 10:50:27 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  acknowledgement = ack-nhfb,
}

@Proceedings{Aiello:2008:AMW,
  editor =       "William Anthony Aiello and others",
  booktitle =    "{Algorithms and models for the web-graph: fourth
                 international workshop, WAW 2006, Banff, Canada,
                 November 30--December 1, 2006: revised papers}",
  title =        "{Algorithms and models for the web-graph: fourth
                 international workshop, WAW 2006, Banff, Canada,
                 November 30--December 1, 2006: revised papers}",
  volume =       "4936",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "x + 165",
  year =         "2008",
  ISBN =         "3-540-78808-5, 3-540-78807-7",
  ISBN-13 =      "978-3-540-78808-9, 978-3-540-78807-2",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA76.9.A43 W39 2006",
  bibdate =      "Thu May 6 08:22:51 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  series =       ser-LNCS,
  acknowledgement = ack-nhfb,
  meetingname =  "WAW 2006 (2006: Banff, Alta.)",
  subject =      "computer algorithms; congresses; data mining; computer
                 science; data mining and knowledge discovery;
                 information systems applications (including Internet)",
}

@Proceedings{IEEE:2008:ICD,
  editor =       "{IEEE}",
  booktitle =    "{47th IEEE Conference on Decision and Control, 2008.
                 CDC 2008. 9--11 December 2008, Cancun, Mexico}",
  title =        "{47th IEEE Conference on Decision and Control, 2008.
                 CDC 2008. 9--11 December 2008, Cancun, Mexico}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2008",
  ISBN =         "1-4244-3123-9",
  ISBN-13 =      "978-1-4244-3123-6",
  LCCN =         "????",
  bibdate =      "Thu May 6 10:51:18 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4721212",
  acknowledgement = ack-nhfb,
}

@Proceedings{IEEE:2008:PIC,
  editor =       "{IEEE}",
  booktitle =    "{Proceedings of the International Conference on
                 Computer Science and Information Technology: August
                 29--September 2, 2008, Singapore. ICCSIT '08}",
  title =        "{Proceedings of the International Conference on
                 Computer Science and Information Technology: August
                 29--September 2, 2008, Singapore. ICCSIT '08}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxiii + 994",
  year =         "2008",
  ISBN =         "0-7695-3308-6",
  ISBN-13 =      "978-0-7695-3308-7",
  LCCN =         "QA75.5 2008",
  bibdate =      "Thu May 6 09:42:05 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P3308.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4624812",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Computer Science and
                 Information Technology (2008: Singapore)",
  subject =      "computer science; congresses; information technology",
}

@Proceedings{IEEE:2008:PII,
  editor =       "{IEEE}",
  booktitle =    "{Proceedings of 2008 IEEE International Conference on
                 Networking, Sensing, and Control: Sanya, China, April
                 6--8, 2008}",
  title =        "{Proceedings of 2008 IEEE International Conference on
                 Networking, Sensing, and Control: Sanya, China, April
                 6--8, 2008}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "1855",
  year =         "2008",
  ISBN =         "1-4244-1685-X",
  ISBN-13 =      "978-1-4244-1685-1",
  LCCN =         "TK5105.5 2008",
  bibdate =      "Thu May 6 09:44:41 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE catalog number CFP08NSC-PRT.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4489617",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE International Conference on Networking, Sensing,
                 and Control (2008: Sanya Shi, China)",
  subject =      "computer networks; congresses; detectors; control
                 theory; artificial intelligence; transportation; mobile
                 communication systems",
}

@Proceedings{Lenzerini:2008:PTS,
  editor =       "Maurizio Lenzerini and Domenico Lembo",
  booktitle =    "{Proceedings of the Twenty-Seventh ACM
                 SIGMOD-SIGACT-SIGART Symposium on Principles of
                 Database Systems: PODS'08, Vancouver, BC, Canada, June
                 9--11, 2008}",
  title =        "{Proceedings of the Twenty-Seventh ACM
                 SIGMOD-SIGACT-SIGART Symposium on Principles of
                 Database Systems: PODS'08, Vancouver, BC, Canada, June
                 9--11, 2008}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "xi + 313",
  year =         "2008",
  ISBN =         "1-59593-685-8",
  ISBN-13 =      "978-1-59593-685-1",
  LCCN =         "????",
  bibdate =      "Fri Jun 20 13:10:29 MDT 2008",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  acknowledgement = ack-nhfb,
  meetingname =  "ACM SIGACT-SIGMOD-SIGART Symposium on Principles of
                 Database Systems (26th: 2007: Beijing, China)",
  xxnote =       "Check ISBN: OCLC has it assigned to the 26th
                 conference in Beijing.",
}

@Proceedings{Ma:2008:FFI,
  editor =       "Jun Ma and others",
  booktitle =    "{FSKD 2008: Fifth International Conference on Fuzzy
                 Systems and Knowledge Discovery: 18--20 October, 2008:
                 Jinan, Shandong, China. FSKD '08}",
  title =        "{FSKD 2008: Fifth International Conference on Fuzzy
                 Systems and Knowledge Discovery: 18--20 October, 2008:
                 Jinan, Shandong, China. FSKD '08}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xix + 658 (vol. 1), xix + 642 (vol. 2), xiv + 687
                 (vol. 3), xx + 706 (vol. 4), xiv + 697 (vol. 5)",
  year =         "2008",
  ISBN =         "0-7695-3305-1",
  ISBN-13 =      "978-0-7695-3305-6",
  LCCN =         "QA248 2008; TJ212.2 .I143 2008",
  bibdate =      "Thu May 6 09:47:01 MDT 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "Five volumes. IEEE Computer Society order number
                 P3305.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4665920",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Fuzzy Systems and
                 Knowledge Discovery (5th: 2008: Jinan, Shandong Sheng,
                 China)",
  subject =      "fuzzy systems; congresses; expert systems (computer
                 science)",
}

@Proceedings{Avrachenkov:2009:AMW,
  editor =       "Konstantin E. Avrachenkov and Debora Donato and Nelly
                 Litvak",
  booktitle =    "{Algorithms and models for the web-graph: 6th
                 international workshop, WAW 2009 Barcelona, Spain,
                 February 12--13, 2009 proceedings}",
  title =        "{Algorithms and models for the web-graph: 6th
                 international workshop, WAW 2009 Barcelona, Spain,
                 February 12--13, 2009 proceedings}",
  volume =       "5427",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "x + 183",
  year =         "2009",
  ISBN =         "3-540-95995-5, 3-540-95994-7 (softcover)",
  ISBN-13 =      "978-3-540-95995-3, 978-3-540-95994-6 (softcover)",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA76.9.A43 W39 2009",
  bibdate =      "Thu May 6 17:32:37 MDT 2010",
  bibsource =    "felix.us.ohio-state.edu:210/INNOPAC;
                 http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  series =       ser-LNCS,
  acknowledgement = ack-nhfb,
}

@Proceedings{Deng:2009:FAT,
  editor =       "Xiaotie Deng and John E. Hopcroft and Jinyun Xue",
  booktitle =    "{Frontiers in algorithmics. Third international
                 workshop, FAW 2009, Hefei, China, June 20--23, 2009.
                 Proceedings}",
  title =        "{Frontiers in algorithmics. Third international
                 workshop, FAW 2009, Hefei, China, June 20--23, 2009.
                 Proceedings}",
  volume =       "5598",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "xiv + 372",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1007/978-3-642-02270-8",
  ISBN =         "3-642-02270-7, 3-642-02269-3",
  ISBN-13 =      "978-3-642-02270-8, 978-3-642-02269-2",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  LCCN =         "QA76.9.A43 F39 2009",
  bibdate =      "Thu May 6 11:19:59 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  series =       ser-LNCS,
  URL =          "http://www.springerlink.com/content/p1040n9jt618;
                 http://www.zentralblatt-math.org/zmath/en/search/?an=1166.68003",
  acknowledgement = ack-nhfb,
  subject =      "algorithms; computational complexity; computer
                 communication networks; computer science; computer
                 software; data mining; software engineering",
  xxeditor =     "David Hutchison and Bernhard Steffen and Doug Tygar
                 and Takeo Kanade and Josef Kittler and Jon M. Kleinberg
                 and Friedemann Mattern and John C. Mitchell and Jinyun
                 Xue and Oscar Nierstrasz and Madhu Sudan and John E.
                 Hopcroft and Xiaotie Deng and Gerhard Weikum and Moshe
                 Y. Vardi and C. {Pandu Rangan} and Demetri Terzopoulos
                 and Moni Nao",
}

@Proceedings{IEEE:2009:IIC,
  editor =       "{IEEE}",
  booktitle =    "{IEEE International Conference on Fuzzy Systems, 2009:
                 FUZZ-IEEE 2009. 20--24 Aug. 2009, ICC Jeju, Jeju
                 Island, Korea}",
  title =        "{IEEE International Conference on Fuzzy Systems, 2009:
                 FUZZ-IEEE 2009. 20--24 Aug. 2009, ICC Jeju, Jeju
                 Island, Korea}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2009",
  ISBN =         "1-4244-3596-X",
  ISBN-13 =      "978-1-4244-3596-8",
  LCCN =         "????",
  bibdate =      "Thu May 6 10:00:09 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=5247842",
  acknowledgement = ack-nhfb,
  remark =       "Parallel als Druckausg. erschienen.",
}

@Proceedings{IEEE:2009:PWW,
  editor =       "{IEEE}",
  booktitle =    "{Proceedings of the 2009 WRI World Congress on
                 Computer Science and Information Engineering: 31
                 March--2 April 2009, Los Angeles, California USA}",
  title =        "{Proceedings of the 2009 WRI World Congress on
                 Computer Science and Information Engineering: 31
                 March--2 April 2009, Los Angeles, California USA}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "????",
  year =         "2009",
  ISBN =         "0-7695-3507-0",
  ISBN-13 =      "978-0-7695-3507-4",
  LCCN =         "QA75.5 2009",
  bibdate =      "Thu May 6 09:49:49 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P3507.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=5170260",
  acknowledgement = ack-nhfb,
  meetingname =  "WRI World Congress on Computer Science and Information
                 Engineering (2009: Los Angeles, Calif.)",
  subject =      "computer science; congresses",
}

@Book{Rousseau:2009:MT,
  editor =       "Christiane Rousseau and Yvan Saint-Aubin",
  booktitle =    "Math{\'e}matiques et Technologie",
  title =        "Math{\'e}matiques et Technologie",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "????",
  year =         "2009",
  DOI =          "http://dx.doi.org/10.1007/978-0-387-69213-5",
  ISBN =         "0-387-69213-4",
  ISBN-13 =      "978-0-387-69213-5",
  LCCN =         "????",
  bibdate =      "Tue Jul 20 16:39:29 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  series =       "Springer Undergraduate Texts in Mathematics and
                 Technology",
  URL =          "http://d-nb.info/997902213/34;
                 http://nbn-resolving.de/urn:nbn:de:1111-20091103138;
                 http://www.springerlink.com/content/r61844",
  acknowledgement = ack-nhfb,
  language =     "French",
  subject =      "Computer science; Distribution (Probability theory);
                 Mathematics; PageRank",
}

@Proceedings{Sohn:2009:FIC,
  editor =       "Sungwon Sohn",
  booktitle =    "{2009 Fourth International Conference on Computer
                 Sciences and Convergence Information Technology: (ICCIT
                 2009). Seoul, Korea, 24--26 November 2009}",
  title =        "{2009 Fourth International Conference on Computer
                 Sciences and Convergence Information Technology: (ICCIT
                 2009). Seoul, Korea, 24--26 November 2009}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  year =         "2009",
  ISBN =         "1-4244-5244-9",
  ISBN-13 =      "978-1-4244-5244-6",
  LCCN =         "????",
  bibdate =      "Thu May 6 10:56:31 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=5367867",
  acknowledgement = ack-nhfb,
}

@Proceedings{Yu:2009:AFS,
  editor =       "Jian Yu",
  booktitle =    "{Advances in fuzzy sets and knowledge discovery: [The
                 Fourth International Conference on Fuzzy Systems and
                 Knowledge Discovery (FSKD'07) was held \ldots{} from
                 24--27 August 2007 in Haikou, Hainan, China]}",
  title =        "{Advances in fuzzy sets and knowledge discovery: [The
                 Fourth International Conference on Fuzzy Systems and
                 Knowledge Discovery (FSKD'07) was held \ldots{} from
                 24--27 August 2007 in Haikou, Hainan, China]}",
  volume =       "57.2009,6",
  publisher =    pub-ELSEVIER,
  address =      pub-ELSEVIER:adr,
  pages =        "865--1072",
  year =         "2009",
  LCCN =         "????",
  bibdate =      "Thu May 6 10:43:28 MDT 2010",
  bibsource =    "http://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
  series =       "Computers and mathematics with applications",
  acknowledgement = ack-nhfb,
}