# bibtab

bibtype: article
filename: pagerank.bib
label: Bini:2010:CAE
author: Dario A. Bini and Gianna M. Del Corso and F. Romani
title: A combined approach for evaluating papers, authors and scientific journals
journal: j-J-COMPUT-APPL-MATH
volume: 234
number: 11
pages: 3104--3121
day: 1
month: oct
monthnumber: 10
year: 2010
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2010.02.003
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Wed Aug 12 08:08:51 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.12 08:08:51 ???
URL: http://www.sciencedirect.com/science/article/pii/S0377042710000749
fjournal: Journal of Computational and Applied Mathematics
keywords: PageRank
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Dario A. Bini and Gianna M. {Del Corso} and F. Romani
rawtitle: A combined approach for evaluating papers, authors and scientific journals
entry:
@Article{Bini:2010:CAE,
  author =       "Dario A. Bini and Gianna M. {Del Corso} and F.
                 Romani",
  title =        "A combined approach for evaluating papers, authors and
                 scientific journals",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "234",
  number =       "11",
  pages =        "3104--3121",
  day =          "1",
  month =        oct,
  year =         "2010",
  CODEN =        "JCAMDI",
  DOI =          "https://doi.org/10.1016/j.cam.2010.02.003",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Wed Aug 12 08:08:51 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042710000749",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
  keywords =     "PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/ICISS.2010.5657148
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5657148
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5643978
rawauthor: Yao Chen and Wenjun Xiong and Jinhu Lu and D. W. C. Ho
rawbooktitle: {2010 International Conference on Intelligent Computing and Integrated Systems (ICISS)}
rawtitle: Pinning scheme for complex networks based on {PageRank} Algorithm
entry:
@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 =          "https://doi.org/10.1109/ICISS.2010.5657148",
  ISBN =         "",
  ISBN-13 =      "",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: Cicone:2010:GPP
author: Antonio Cicone and Stefano Serra-Capizzano
title: Google PageRanking problem: the model and the analysis
journal: j-J-COMPUT-APPL-MATH
volume: 234
number: 11
pages: 3140--3169
day: 1
month: oct
monthnumber: 10
year: 2010
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Sat Feb 25 13:24:23 MST 2017
bibtimestamp: 2017.02.25 13:24:23 MST
URL: http://www.sciencedirect.com/science/article/pii/S0377042710000762
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Antonio Cicone and Stefano Serra-Capizzano
rawtitle: {Google} {PageRanking} problem: the model and the analysis
bibsource:
https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Cicone:2010:GPP,
  author =       "Antonio Cicone and Stefano Serra-Capizzano",
  title =        "{Google} {PageRanking} problem: the model and the
                 analysis",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "234",
  number =       "11",
  pages =        "3140--3169",
  day =          "1",
  month =        oct,
  year =         "2010",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Sat Feb 25 13:24:23 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042710000762",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
}


bibtype: book
filename: pagerank.bib
label: 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 OR 0470562315
ISBN13: 978-0-470-56231-4 OR 9780470562314
LCCN: TK5105.885.G66 C55 2010eb
bibdate: Fri Jun 3 09:52:48 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2011.06.03 09:52:48 MDT
subject: Google Analytics; Web usage mining; Internet users; Statistics; Data processing
acknowledgement: ack-nhfb
rawauthor: Brian Clifton
rawtitle: Advanced {Web} metrics with {Google Analytics}
entry:
@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 =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: Constantine:2010:RAP
author: P. G. Constantine and D. F. Gleich
title: Random alpha PageRank
journal: j-INTERNET-MATH
volume: 6
number: 2
pages: 189--236
year: 2010
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
bibdate: Tue Aug 11 16:34:18 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:34:18 ???
URL: http://projecteuclid.org/euclid.im/1285339073
fjournal: Internet Mathematics
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: P. G. Constantine and D. F. Gleich
rawtitle: Random alpha {PageRank}
entry:
@Article{Constantine:2010:RAP,
  author =       "P. G. Constantine and D. F. Gleich",
  title =        "Random alpha {PageRank}",
  journal =      j-INTERNET-MATH,
  volume =       "6",
  number =       "2",
  pages =        "189--236",
  month =        "????",
  year =         "2010",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  bibdate =      "Tue Aug 11 16:34:18 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/euclid.im/1285339073",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: book
filename: pagerank.bib
label: 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) OR 0131364898 (paperback)
ISBN13: 978-0-13-136489-9 (paperback) OR 9780131364899 (paperback)
LCCN: TK5105.884 CRO 2010
bibdate: Thu May 5 19:23:28 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; library.ox.ac.uk:210/ADVANCE
bibtimestamp: 2011.05.05 19:23:28 MDT
subject: Search engines; Information storage and retrieval systems; Information retrieval
acknowledgement: ack-nhfb
rawauthor: W. Bruce Croft and Donald Metzler and Trevor Strohman
rawtitle: Search engines: information retrieval in practice
entry:
@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 =    "https://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",
}


bibtype: techreport
filename: pagerank.bib
label: 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: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 15:07:14 ???
URL: http://arxiv.org/pdf/1002.2858
keywords: bibliometrics; commodity pricing; PageRank; social network analysis; Web information retrieval
acknowledgement: ack-nhfb
rawauthor: Massimo Franceschet
rawtitle: {PageRank}: Stand on the shoulders of giants
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.

rawabstract:
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.

entry:
@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 =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
year: 2010
CODEN: SJOCE3
DOI: https://doi.org/10.1137/080727397
ISSN: 1064-8275 (print), 1095-7197 (electronic) OR 10648275 (print), 10957197 (electronic)
ISSNL: 1064-8275 OR 10648275
bibdate: Wed May 19 10:44:24 MDT 2010
bibsource: http://epubs.siam.org/sam-bin/dbq/toc/SISC/32/1; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.19 10:44:24 MDT
fjournal: SIAM Journal on Scientific Computing
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sisc
rawauthor: David F. Gleich and Andrew P. Gray and Chen Greif and Tracy Lau
rawtitle: An Inner-Outer Iteration for Computing {PageRank}
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.

rawabstract:
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.

entry:
@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 =          "https://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;
                 https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Gleich:2010:TRS
author: David F. Gleich and Paul G. Constantine and Abraham Flaxman and Asela Gunawardana
editor: Michael Rappa and Paul Jones
title: Tracking the random surfer: Empirically measured teleportation parameters in PageRank
publisher: pub-ACM
address: pub-ACM:adr
pages: 381--390
year: 2010
DOI: https://doi.org/10.1145/1772690.1772730
ISBN: 1-60558-799-0 OR 1605587990
ISBN13: 978-1-60558-799-8 OR 9781605587998
LCCN: TK5105.888 .I573 2010eb
bibdate: Tue Aug 11 16:45:55 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:45:55 ???
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1145/1772690
bookpages: 41 + 1386
rawauthor: David F. Gleich and Paul G. Constantine and Abraham Flaxman and Asela Gunawardana
raweditor: Michael Rappa and Paul Jones
rawtitle: Tracking the random surfer: Empirically measured teleportation parameters in {PageRank}
booktitle:
Proceedings of the 19th International Conference on World Wide Web: Raleigh, North Carolina, USA, April 26--30, 2010

rawbooktitle:
{Proceedings of the 19th International Conference on World Wide Web: Raleigh, North Carolina, USA, April 26--30, 2010}

entry:
@InProceedings{Gleich:2010:TRS,
  author =       "David F. Gleich and Paul G. Constantine and Abraham
                 Flaxman and Asela Gunawardana",
  editor =       "Michael Rappa and Paul Jones",
  booktitle =    "{Proceedings of the 19th International Conference on
                 World Wide Web: Raleigh, North Carolina, USA, April
                 26--30, 2010}",
  title =        "Tracking the random surfer: Empirically measured
                 teleportation parameters in {PageRank}",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "381--390",
  year =         "2010",
  DOI =          "https://doi.org/10.1145/1772690.1772730",
  ISBN =         "1-60558-799-0",
  ISBN-13 =      "978-1-60558-799-8",
  LCCN =         "TK5105.888 .I573 2010eb",
  bibdate =      "Tue Aug 11 16:45:55 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  book-DOI =     "https://doi.org/10.1145/1772690",
  bookpages =    "41 + 1386",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/ICEE.2010.1259
ISBN: 0-7695-3997-1 OR 0769539971
ISBN13: 978-0-7695-3997-3 OR 9780769539973
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5592871
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5589107
rawauthor: Xiaojun He and Yibing Li and Chunxiao Fan
rawbooktitle: {2010 International Conference on E-Business and E-Government (ICEE)}
rawtitle: {Web}-Based Links and Authoritative Content {Pagerank} Improvement
entry:
@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 =          "https://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 =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
year: 2010
CODEN: IETAA9
DOI: https://doi.org/10.1109/TAC.2010.2042984
ISSN: 0018-9286 OR 00189286
ISSNL: 0018-9286 OR 00189286
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5411738
fjournal: IEEE Transactions on Automatic Control
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9
rawauthor: H. Ishii and R. Tempo
rawtitle: Distributed Randomized Algorithms for the {PageRank} Computation
entry:
@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 =          "https://doi.org/10.1109/TAC.2010.2042984",
  ISSN =         "0018-9286",
  ISSN-L =       "0018-9286",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/CDC.2010.5718041
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5718041
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5707200
rawauthor: H. Ishii and R. Tempo and E. Bai
rawbooktitle: {2010 49th IEEE Conference on Decision and Control (CDC)}
rawtitle: Distributed randomized pagerank algorithms based on web aggregation over unreliable channels
entry:
@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 =          "https://doi.org/10.1109/CDC.2010.5718041",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Jain:2010:FRW
author: Alpa Jain and Patrick Pantel
editor: Chu-Ren Huang and Dan Jurafsky
title: FactRank: Random walks on a web of facts
publisher: Tsinghua University Press
address: Block A, Xue Yan Building, Tsinghua University, Beijing, 100084, China
pages: 501--509
month: aug
monthnumber: 8
year: 2010
bibdate: Tue Aug 11 17:06:19 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:06:19 ???
URL: http://delivery.acm.org/10.1145/1880000/1873838/p501-jain.pdf
acknowledgement: ack-nhfb
rawauthor: Alpa Jain and Patrick Pantel
raweditor: Chu-Ren Huang and Dan Jurafsky
rawtitle: {FactRank}: Random walks on a web of facts
booktitle:
COLING'10: 23rd International Conference on Computational Linguistics, Proceedings, 23--27 August 2010, Beijing International Convention Center, Beijing, China

rawbooktitle:
{COLING'10: 23rd International Conference on Computational Linguistics, Proceedings, 23--27 August 2010, Beijing International Convention Center, Beijing, China}

entry:
@InProceedings{Jain:2010:FRW,
  author =       "Alpa Jain and Patrick Pantel",
  editor =       "Chu-Ren Huang and Dan Jurafsky",
  booktitle =    "{COLING'10: 23rd International Conference on
                 Computational Linguistics, Proceedings, 23--27 August
                 2010, Beijing International Convention Center, Beijing,
                 China}",
  title =        "{FactRank}: Random walks on a web of facts",
  publisher =    "Tsinghua University Press",
  address =      "Block A, Xue Yan Building, Tsinghua University,
                 Beijing, 100084, China",
  pages =        "501--509",
  month =        aug,
  year =         "2010",
  ISBN =         "????",
  ISBN-13 =      "????",
  LCCN =         "????",
  bibdate =      "Tue Aug 11 17:06:19 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://delivery.acm.org/10.1145/1880000/1873838/p501-jain.pdf",
  acknowledgement = ack-nhfb,
  xxaddress =    "Stroudsburg, PA, USA",
  xxpublisher =  "Association for Computational Linguistics",
}


bibtype: article
filename: pagerank.bib
label: 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-COMPUT-MATH-APPL
volume: 60
number: 3
pages: 873--889
month: aug
monthnumber: 8
year: 2010
CODEN: CMAPDK
DOI: https://doi.org/10.1016/j.camwa.2010.05.034
ISSN: 0898-1221 (print), 1873-7668 (electronic) OR 08981221 (print), 18737668 (electronic)
ISSNL: 0898-1221 OR 08981221
bibdate: Thu Dec 29 08:18:39 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.12.29 08:18:39 ???
URL: http://www.sciencedirect.com/science/article/pii/S0898122110003913
fjournal: Computers and Mathematics with Applications
acknowledgement: ack-nhfb
rawauthor: Wei Jiang and Gang Wu
rawtitle: A thick-restarted block {Arnoldi} algorithm with modified {Ritz} vectors for large eigenproblems
entry:
@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-COMPUT-MATH-APPL,
  volume =       "60",
  number =       "3",
  pages =        "873--889",
  month =        aug,
  year =         "2010",
  CODEN =        "CMAPDK",
  DOI =          "https://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 =    "https://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",
}


bibtype: book
filename: pagerank.bib
label: 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) OR 0691145032 (hardcover)
ISBN13: 978-0-691-14503-7 (hardcover) OR 9780691145037 (hardcover)
LCCN: ZA4460 .K36 2010
bibdate: Mon Jun 13 18:50:45 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.loc.gov:7090/Voyager
bibtimestamp: 2011.06.13 18:50:45 MDT
acknowledgement: ack-nhfb
rawauthor: Sep Kamvar
rawtitle: Numerical algorithms for personalized search in self-organizing information networks
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

subject:
Database searching; Mathematics; Information networks; Content analysis (Communication); Self-organizing systems; Data processing; Algorithms; Internet searching

TOC:
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

rawabstract:
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

rawTOC:
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

entry:
@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 =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 11
year: 2010
CODEN: ATISET
DOI: https://doi.org/10.1145/1852102.1852104
ISSN: 1046-8188 OR 10468188
ISSNL: 0734-2047 OR 07342047
bibdate: Tue Nov 23 10:24:49 MST 2010
bibsource: http://www.acm.org/pubs/contents/journals/tois/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.11.23 10:24:49 MST
fjournal: ACM Transactions on Information Systems (TOIS)
acknowledgement: ack-nhfb
articleno: 18
rawauthor: Oren Kurland and Lillian Lee
rawtitle: {PageRank} without hyperlinks: {Structural} reranking using links induced by language models
entry:
@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 =          "https://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/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  articleno =    "18",
  fjournal =     "ACM Transactions on Information Systems (TOIS)",
}


bibtype: book
filename: pagerank.bib
label: 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 OR 0470531282, 0470874007
ISBN13: 978-0-470-53128-0, 978-0-470-87400-4 OR 9780470531280, 9780470874004
LCCN: TK5105.885.G66 L43 2010eb
bibdate: Fri Jun 3 09:52:48 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2011.06.03 09:52:48 MDT
acknowledgement: ack-nhfb
rawauthor: Jerri L. Ledford and Joe Teixeira and Mary E. Tyler
rawtitle: {Google Analytics}
subject:
Google Analytics; Internet searching; Statistical services; Web usage mining; Computer programs; Internet users; Statistics; Data processing

TOC:
. Part 1. Getting started with Google Analytics Part 2. Analytics and site statistics: concepts and methods Part 3. Advanced implementation Part 4. The reports

rawTOC:
. Part 1. Getting started with Google Analytics \\ Part 2. Analytics and site statistics: concepts and methods \\ Part 3. Advanced implementation \\ Part 4. The reports

entry:
@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 =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: Levy:2010:HGA
author: Steven Levy
title: How Google's algorithm rules the Web
journal: j-WIRED
volume: 17
pages: ??--??
day: 2
month: feb
monthnumber: 2
year: 2010
CODEN: WREDEM
ISSN: 1059-1028 (print), 1078-3148 (electronic) OR 10591028 (print), 10783148 (electronic)
ISSNL: 1059-1028 OR 10591028
bibdate: Tue Aug 11 17:21:08 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:21:08 ???
URL: http://www.wired.com/2010/02/ff_google_algorithm/
fjournal: Wired
acknowledgement: ack-nhfb
journalURL: http://www.wired.com
rawauthor: Steven Levy
rawtitle: How {Google}'s algorithm rules the {Web}
entry:
@Article{Levy:2010:HGA,
  author =       "Steven Levy",
  title =        "How {Google}'s algorithm rules the {Web}",
  journal =      j-WIRED,
  volume =       "17",
  pages =        "??--??",
  day =          "2",
  month =        feb,
  year =         "2010",
  CODEN =        "WREDEM",
  ISSN =         "1059-1028 (print), 1078-3148 (electronic)",
  ISSN-L =       "1059-1028",
  bibdate =      "Tue Aug 11 17:21:08 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.wired.com/2010/02/ff_google_algorithm/",
  acknowledgement = ack-nhfb,
  fjournal =     "Wired",
  journal-URL =  "http://www.wired.com",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/ICEEE.2010.5660630
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5660630
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5660084
rawauthor: Zhengyang Liu and Jianyi Liu and Wenbin Yao and Cong Wang
rawbooktitle: {2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE)}
rawtitle: Keyword Extraction Using {PageRank} on Synonym Networks
entry:
@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 =          "https://doi.org/10.1109/ICEEE.2010.5660630",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/ICCET.2010.5485388
ISBN: 1-4244-6347-5 OR 1424463475
ISBN13: 978-1-4244-6347-3 OR 9781424463473
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5485388
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5473895
rawauthor: Dongfei Liu and Yong Gong
rawbooktitle: {2010 2nd International Conference on Computer Engineering and Technology (ICCET)}
rawtitle: Optimal methods of {PageRank} algorithm on the bilingual web page
entry:
@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 =          "https://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 =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/WISM.2010.63
ISBN: 1-4244-8438-3 OR 1424484383
ISBN13: 978-1-4244-8438-6 OR 9781424484386
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5662302
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5661667
rawauthor: Haibo Ma and Shiyong Chen and Deguang Wang
rawbooktitle: {2010 International Conference on Web Information Systems and Mining (WISM)}
rawtitle: Research of {PageRank} Algorithm Based on Transition Probability
entry:
@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 =          "https://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 =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/ReConFig.2010.83
ISBN: 1-4244-9523-7 OR 1424495237
ISBN13: 978-1-4244-9523-8 OR 9781424495238
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5695315
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5692850
rawauthor: S. McGettrick and D. Geraghty
rawbooktitle: {2010 International Conference on Reconfigurable Computing and FPGAs (ReConFig)}
rawtitle: Hardware Computation of the {PageRank} Eigenvector
entry:
@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 =          "https://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 =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Nazin:2010:EPE
author: A. Nazin
booktitle: 2010 49th IEEE Conference on Decision and Control (CDC)
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 792--797
year: 2010
DOI: https://doi.org/10.1109/CDC.2010.5717923
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5717923
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5707200
rawauthor: A. Nazin
rawbooktitle: {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

rawtitle:
Estimating the principal eigenvector of a stochastic matrix: Mirror Descent Algorithms via game approach with application to {PageRank} problem

entry:
@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 =          "https://doi.org/10.1109/CDC.2010.5717923",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Jun 3 12:50:37 MDT 2011",
  bibsource =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/NSS.2010.12
ISBN: 1-4244-8484-7 OR 1424484847
ISBN13: 978-1-4244-8484-3 OR 9781424484843
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5635820
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5634608
rawauthor: Bing-Yuan Pu and Ting-Zhu Huang and Chun Wen
rawbooktitle: {2010 4th International Conference on Network and System Security (NSS)}
rawtitle: An Improved {PageRank} Algorithm: Immune to Spam
entry:
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  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 =          "https://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 =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/IWISA.2010.5473657
ISBN: 1-4244-5872-2 OR 1424458722
ISBN13: 978-1-4244-5872-1 OR 9781424458721
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5473657
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5472913
rawauthor: Yongbin Qin and Daoyun Xu
rawbooktitle: {2010 2nd International Workshop on Intelligent Systems and Applications (ISA)}
rawtitle: A Balanced Rank Algorithm Based on {PageRank} and Page Belief Recommendation
entry:
@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 =          "https://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 =    "https://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",
}


bibtype: book
filename: pagerank.bib
label: 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) OR 1607418711 (hardcover)
ISBN13: 978-1-60741-871-9 (hardcover) OR 9781607418719 (hardcover)
LCCN: KF2994 .C62 2010
bibdate: Fri Jun 3 09:47:20 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.loc.gov:7090/Voyager
bibtimestamp: 2011.06.03 09:47:20 MDT
series: Laws and legislation
subject: Copyright; United States; Fair use (Copyright)
acknowledgement: ack-nhfb
raweditor: Brett D. Rhodes
rawtitle: Copyright law and a brief look at the {Google Library Project}
TOC:
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

rawTOC:
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

entry:
@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 =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: Shepelyansky:2010:GMD
author: D. L. Shepelyansky and O. V. Zhirov
title: Google matrix, dynamical attractors, and Ulam networks
journal: j-PHYS-REV-E
volume: 81
number: 3
pages: 036213:1--036213:9
month: mar
monthnumber: 3
year: 2010
CODEN: PLEEE8
DOI: https://doi.org/10.1103/PhysRevE.81.036213
ISSN: 1539-3755 (print), 1550-2376 (electronic) OR 15393755 (print), 15502376 (electronic)
ISSNL: 1539-3755 OR 15393755
bibdate: Tue Aug 11 17:34:23 2015
bibtimestamp: 2015.08.11 17:34:23 ???
fjournal: Physical Review E (Statistical physics, plasmas, fluids, and related interdisciplinary topics)
acknowledgement: ack-nhfb
journalURL: http://pre.aps.org/browse
rawauthor: D. L. Shepelyansky and O. V. Zhirov
rawtitle: {Google} matrix, dynamical attractors, and {Ulam} networks
bibsource:
https://www.math.utah.edu/pub/bibnet/authors/u/ulam-stanislaw-m.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

URL:
http://journals.aps.org/pre/abstract/10.1103/PhysRevE.81.036213; http://link.aps.org/doi/10.1103/PhysRevE.81.036213

entry:
@Article{Shepelyansky:2010:GMD,
  author =       "D. L. Shepelyansky and O. V. Zhirov",
  title =        "{Google} matrix, dynamical attractors, and {Ulam}
                 networks",
  journal =      j-PHYS-REV-E,
  volume =       "81",
  number =       "3",
  pages =        "036213:1--036213:9",
  month =        mar,
  year =         "2010",
  CODEN =        "PLEEE8",
  DOI =          "https://doi.org/10.1103/PhysRevE.81.036213",
  ISSN =         "1539-3755 (print), 1550-2376 (electronic)",
  ISSN-L =       "1539-3755",
  bibdate =      "Tue Aug 11 17:34:23 2015",
  bibsource =    "https://www.math.utah.edu/pub/bibnet/authors/u/ulam-stanislaw-m.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://journals.aps.org/pre/abstract/10.1103/PhysRevE.81.036213;
                 http://link.aps.org/doi/10.1103/PhysRevE.81.036213",
  acknowledgement = ack-nhfb,
  fjournal =     "Physical Review E (Statistical physics, plasmas,
                 fluids, and related interdisciplinary topics)",
  journal-URL =  "http://pre.aps.org/browse",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/ITCS.2010.68
ISBN: 1-4244-7293-8 OR 1424472938
ISBN13: 978-1-4244-7293-2 OR 9781424472932
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5557139
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5556872
rawauthor: Deguang Wang and Zhigang Zhou and Haibo Ma
rawbooktitle: {2010 Second International Conference on Information Technology and Computer Science (ITCS)}
rawtitle: Application of {PageRank} Algorithm in Computer Forensics
entry:
@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 =          "https://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 =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: Weng:2010:TFT
author: Jianshu Weng and Ee-Peng Lim and Jing Jiang and Qi He
editor: Brian D. Davison and Torsten Suel
title: TwitterRank: Finding topic-sensitive influential twitterers
publisher: pub-ACM
address: pub-ACM:adr
pages: 261--270
year: 2010
DOI: https://doi.org/10.1145/1718487.1718520
ISBN: 1-60558-889-X OR 160558889X
ISBN13: 978-1-60558-889-6 OR 9781605588896
LCCN: QA76.9.D343 I5838 2010
bibdate: Tue Aug 11 17:45:37 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:45:37 ???
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1145/1718487
bookURL: http://portal.acm.org/toc.cfm?id=1718487
bookpages: xii + 450
rawauthor: Jianshu Weng and Ee-Peng Lim and Jing Jiang and Qi He
raweditor: Brian D. Davison and Torsten Suel
rawtitle: {TwitterRank}: Finding topic-sensitive influential twitterers
booktitle:
WSDM: proceedings of the third ACM International Conference on Web Search and Data Mining: February 3--6, 2010, New York City, NY, USA

rawbooktitle:
{WSDM: proceedings of the third ACM International Conference on Web Search and Data Mining: February 3--6, 2010, New York City, NY, USA}

entry:
@InProceedings{Weng:2010:TFT,
  author =       "Jianshu Weng and Ee-Peng Lim and Jing Jiang and Qi
                 He",
  editor =       "Brian D. Davison and Torsten Suel",
  booktitle =    "{WSDM: proceedings of the third ACM International
                 Conference on Web Search and Data Mining: February
                 3--6, 2010, New York City, NY, USA}",
  title =        "{TwitterRank}: Finding topic-sensitive influential
                 twitterers",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "261--270",
  year =         "2010",
  DOI =          "https://doi.org/10.1145/1718487.1718520",
  ISBN =         "1-60558-889-X",
  ISBN-13 =      "978-1-60558-889-6",
  LCCN =         "QA76.9.D343 I5838 2010",
  bibdate =      "Tue Aug 11 17:45:37 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  book-DOI =     "https://doi.org/10.1145/1718487",
  book-URL =     "http://portal.acm.org/toc.cfm?id=1718487",
  bookpages =    "xii + 450",
}


bibtype: article
filename: pagerank.bib
label: Wu:2010:AEA
author: Gang Wu and Yimin Wei
title: An Arnoldi-extrapolation algorithm for computing PageRank
journal: j-J-COMPUT-APPL-MATH
volume: 234
number: 11
pages: 3196--3212
day: 1
month: oct
monthnumber: 10
year: 2010
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Sat Feb 25 13:24:23 MST 2017
bibtimestamp: 2017.02.25 13:24:23 MST
URL: http://www.sciencedirect.com/science/article/pii/S0377042710000804
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Gang Wu and Yimin Wei
rawtitle: An {Arnoldi}-extrapolation algorithm for computing {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Wu:2010:AEA,
  author =       "Gang Wu and Yimin Wei",
  title =        "An {Arnoldi}-extrapolation algorithm for computing
                 {PageRank}",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "234",
  number =       "11",
  pages =        "3196--3212",
  day =          "1",
  month =        oct,
  year =         "2010",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Sat Feb 25 13:24:23 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042710000804",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
}


bibtype: article
filename: pagerank.bib
label: Wu:2010:AVG
author: Gang Wu and Yimin Wei
journal: j-TOIS
volume: 28
number: 3
pages: 11:1--11:28
month: jun
monthnumber: 6
year: 2010
CODEN: ATISET
DOI: https://doi.org/10.1145/1777432.1777434
ISSN: 1046-8188 OR 10468188
ISSNL: 0734-2047 OR 07342047
bibdate: Tue Jul 6 15:53:00 MDT 2010
bibsource: http://www.acm.org/pubs/contents/journals/tois/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.07.06 15:53:00 MDT
fjournal: ACM Transactions on Information Systems
keywords: Arnoldi; GMRES; Google; Krylov subspace; PageRank; Web ranking
acknowledgement: ack-nhfb
articleno: 11
rawauthor: Gang Wu and Yimin Wei
title:
Arnoldi versus GMRES for computing PageRank: a theoretical contribution to Google's PageRank problem

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.

rawabstract:
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.

rawtitle:
{Arnoldi} versus {GMRES} for computing {PageRank}: a theoretical contribution to {Google}'s {PageRank} problem

entry:
@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 =          "https://doi.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/;
                 https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/ICPP.2010.17
ISBN: 1-4244-7913-4 OR 1424479134
ISBN13: 978-1-4244-7913-9 OR 9781424479139
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5599152
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5598250
rawauthor: Tianji Wu and Bo Wang and Yi Shan and Feng Yan and Yu Wang and Ningyi Xu
rawbooktitle: {2010 39th International Conference on Parallel Processing (ICPP)}
rawtitle: Efficient {PageRank} and {SpMV} Computation on {AMD} {GPUs}
entry:
@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 =          "https://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 =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: Wu:2010:KSA
author: Gang Wu and Ying Zhang and Yimin Wei
title: Krylov Subspace Algorithms for Computing GeneRank for the Analysis of Microarray Data Mining
journal: j-J-COMPUT-BIOL
volume: 17
number: 4
pages: 631--646
month: apr
monthnumber: 4
year: 2010
CODEN: JCOBEM
DOI: https://doi.org/10.1089/cmb.2009.0004
ISSN: 1066-5277 (print), 1557-8666 (electronic) OR 10665277 (print), 15578666 (electronic)
ISSNL: 1066-5277 OR 10665277
bibdate: Sat Jun 1 09:49:51 MDT 2019
bibtimestamp: 2019.06.01 09:49:51 MDT
fjournal: Journal of Computational Biology
acknowledgement: ack-nhfb
journalURL: https://www.liebertpub.com/loi/cmb/
onlinedate: 28 April 2010
rawauthor: Gang Wu and Ying Zhang and Yimin Wei
rawtitle: {Krylov} Subspace Algorithms for Computing {GeneRank} for the Analysis of Microarray Data Mining
bibsource:
https://www.math.utah.edu/pub/tex/bib/jcomputbiol.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

URL:
https://www.liebertpub.com/doi/abs/10.1089/cmb.2009.0004; https://www.liebertpub.com/doi/pdf/10.1089/cmb.2009.0004

entry:
@Article{Wu:2010:KSA,
  author =       "Gang Wu and Ying Zhang and Yimin Wei",
  title =        "{Krylov} Subspace Algorithms for Computing {GeneRank}
                 for the Analysis of Microarray Data Mining",
  journal =      j-J-COMPUT-BIOL,
  volume =       "17",
  number =       "4",
  pages =        "631--646",
  month =        apr,
  year =         "2010",
  CODEN =        "JCOBEM",
  DOI =          "https://doi.org/10.1089/cmb.2009.0004",
  ISSN =         "1066-5277 (print), 1557-8666 (electronic)",
  ISSN-L =       "1066-5277",
  bibdate =      "Sat Jun 1 09:49:51 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputbiol.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://www.liebertpub.com/doi/abs/10.1089/cmb.2009.0004;
                 https://www.liebertpub.com/doi/pdf/10.1089/cmb.2009.0004",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational Biology",
  journal-URL =  "https://www.liebertpub.com/loi/cmb/",
  onlinedate =   "28 April 2010",
}


bibtype: inproceedings
filename: pagerank.bib
label: Zhang:2010:MSF
author: Yi Zhang and Kaihua Xu and Yuhua Liu and Zhenrong Luo
editor: IEEE
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: https://doi.org/10.1109/ICFCC.2010.5497402
ISBN: 1-4244-5822-6, 1-4244-5821-8 OR 1424458226, 1424458218
ISBN13: 978-1-4244-5822-6, 978-1-4244-5821-9 OR 9781424458226, 9781424458219
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
note: IEEE catalog number CFP1037G-PRT.
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5497402
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5487607
rawauthor: Yi Zhang and Kaihua Xu and Yuhua Liu and Zhenrong Luo
raweditor: {IEEE}
rawnote: IEEE catalog number CFP1037G-PRT.
rawtitle: Modeling of scale-free network based on pagerank algorithm
booktitle:
Proceedings of the 2010 2nd International Conference on Future Computer and Communication: ICFCC 2010, 21-24 May 2010, Wuhan, China

rawbooktitle:
{Proceedings of the 2010 2nd International Conference on Future Computer and Communication: ICFCC 2010, 21-24 May 2010, Wuhan, China}

entry:
@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 =          "https://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 =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/ICISE.2010.5691573
ISBN: 1-4244-7616-X OR 142447616X
ISBN13: 978-1-4244-7616-9 OR 9781424476169
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5691573
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5680733
rawauthor: Ji-Lin Zhang and Yong-jian Ren and Wei Zhang and Xiang-Hua Xu and Jian Wan and Yu Weng
rawbooktitle: {2010 2nd International Conference on Information Science and Engineering (ICISE)}
rawtitle: Webs ranking model based on pagerank algorithm
entry:
@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 =          "https://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 =    "https://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",
}


bibtype: misc
filename: pagerank.bib
label: Anonymous:2011:EOR
author: Anonymous
title: tt eigenfactor.org: Ranking and mapping science
pages: 4811--4814
year: 2011
bibdate: Thu Jun 02 08:43:09 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.02 08:43:09 ???
note: Journal impact ranking.
URL: http://www.eigenfactor.org/
acknowledgement: ack-nhfb
howpublished: Web site.
rawauthor: Anonymous
rawnote: Journal impact ranking.
rawtitle: {{\tt eigenfactor.org}}: Ranking and mapping science
entry:
@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 =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  note =         "Journal impact ranking.",
  URL =          "http://www.eigenfactor.org/",
  acknowledgement = ack-nhfb,
}


bibtype: book
filename: pagerank.bib
label: 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 OR 0789741032
ISBN13: 978-0-7897-4103-5 OR 9780789741035
LCCN: HD9696.8.U64 G6627 2011
bibdate: Fri Jun 3 09:52:48 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2011.06.03 09:52:48 MDT
subject: Electronic commerce; Internet searching; Web search engines; Success in business
acknowledgement: ack-nhfb
rawauthor: Evan Bailyn and Brad Bailyn
rawtitle: Outsmarting {Google}
TOC:
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.

rawTOC:
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.

entry:
@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 =    "https://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.",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/CSSS.2011.5974627
bibdate: Mon Sep 12 21:28:08 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.09.12 21:28:08 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5974627
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5959270
rawauthor: Zhou Cailan and Chen Kai and Li Shasha
rawbooktitle: {2011 International Conference on Computer Science and Service System (CSSS)}
rawtitle: Improved {PageRank} algorithm based on feedback of user clicks
entry:
@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 =          "https://doi.org/10.1109/CSSS.2011.5974627",
  bibdate =      "Mon Sep 12 21:28:08 MDT 2011",
  bibsource =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: Cevahir:2011:SBP
author: Ali Cevahir and Cevdet Aykanat and Ata Turk and B. Barla Cambazogglu
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
monthnumber: 5
year: 2011
CODEN: ITDSEO
DOI: https://doi.org/10.1109/TPDS.2010.119
ISSN: 1045-9219 (print), 1558-2183 (electronic) OR 10459219 (print), 15582183 (electronic)
ISSNL: 1045-9219 OR 10459219
bibdate: Fri Jun 3 12:50:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 12:50:37 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5482570
fjournal: IEEE Transactions on Parallel and Distributed Systems
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=71
journalURL: http://www.computer.org/tpds/archives.htm
rawauthor: Ali Cevahir and Cevdet Aykanat and Ata Turk and B. Barla Cambazo{\u{g}}glu
rawtitle: Site-Based Partitioning and Repartitioning Techniques for Parallel {PageRank} Computation
entry:
@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 =          "https://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 =    "https://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/tpds/archives.htm",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 6
year: 2011
CODEN: VLDBFR
DOI: https://doi.org/10.1007/s00778-010-0204-8
ISSN: 1066-8888 (print), 0949-877X (electronic) OR 10668888 (print), 0949877X (electronic)
ISSNL: 1066-8888 OR 10668888
bibdate: Tue Jun 14 11:27:46 MDT 2011
bibtimestamp: 2011.06.14 11:27:46 MDT
fjournal: VLDB Journal: Very Large Data Bases
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/toc.cfm?id=J869
rawauthor: Soumen Chakrabarti and Amit Pathak and Manish Gupta
rawtitle: Index design and query processing for graph conductance search
bibsource:
http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://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.

rawabstract:
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.

entry:
@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 =          "https://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/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: Chung:2011:DPT
author: Fan Chung and Alexander Tsiatas and Wensong Xu
editor: Alan Frieze and Paul Horn and Pawel Pralat
title: Dirichlet PageRank and trust-based ranking algorithms
journal: j-LECT-NOTES-COMP-SCI
volume: 6732
pages: 103--114
year: 2011
CODEN: LNCSD9
DOI: https://doi.org/10.1007/978-3-642-21286-4_9
ISBN: 3-642-21285-9 (print), 3-642-21286-7 (electronic) OR 3642212859 (print), 3642212867 (electronic)
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
ISSNL: 0302-9743 OR 03029743
bibdate: Tue Aug 11 16:30:03 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:30:03 ???
fjournal: Lecture Notes in Computer Science
acknowledgement: ack-nhfb
bookURL: http://link.springer.com/book/10.1007/978-3-540-95995-3
journalURL: http://link.springer.com/bookseries/558
rawauthor: Fan Chung and Alexander Tsiatas and Wensong Xu
raweditor: Alan Frieze and Paul Horn and Pawe{\l} Pra{\l}at
rawtitle: {Dirichlet PageRank} and trust-based ranking algorithms
booktitle:
Algorithms and Models for the Web Graph: 8th International Workshop, WAW 2011, Atlanta, GA, USA, May 27--29, 2011. Proceedings

ISBN13:
978-3-642-21285-7 (print), 978-3-642-21286-4 (electronic) OR 9783642212857 (print), 9783642212864 (electronic)

rawbooktitle:
{Algorithms and Models for the Web Graph: 8th International Workshop, WAW 2011, Atlanta, GA, USA, May 27--29, 2011. Proceedings}

entry:
@Article{Chung:2011:DPT,
  author =       "Fan Chung and Alexander Tsiatas and Wensong Xu",
  editor =       "Alan Frieze and Paul Horn and Pawe{\l} Pra{\l}at",
  booktitle =    "{Algorithms and Models for the Web Graph: 8th
                 International Workshop, WAW 2011, Atlanta, GA, USA, May
                 27--29, 2011. Proceedings}",
  title =        "{Dirichlet PageRank} and trust-based ranking
                 algorithms",
  journal =      j-LECT-NOTES-COMP-SCI,
  volume =       "6732",
  pages =        "103--114",
  year =         "2011",
  CODEN =        "LNCSD9",
  DOI =          "https://doi.org/10.1007/978-3-642-21286-4_9",
  ISBN =         "3-642-21285-9 (print), 3-642-21286-7 (electronic)",
  ISBN-13 =      "978-3-642-21285-7 (print), 978-3-642-21286-4
                 (electronic)",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  ISSN-L =       "0302-9743",
  bibdate =      "Tue Aug 11 16:30:03 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  book-URL =     "http://link.springer.com/book/10.1007/978-3-540-95995-3",
  fjournal =     "Lecture Notes in Computer Science",
  journal-URL =  "http://link.springer.com/bookseries/558",
}


bibtype: article
filename: pagerank.bib
label: Dayar:2011:SSA
author: Tugrul Dayar and Gokce 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
ISSN: 1068-9613 (print), 1097-4067 (electronic) OR 10689613 (print), 10974067 (electronic)
ISSNL: 1068-9613 OR 10689613
bibdate: Thu Jun 9 12:14:22 MDT 2011
bibsource: http://etna.mcs.kent.edu/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.09 12:14:22 MDT
fjournal: Electronic Transactions on Numerical Analysis
acknowledgement: ack-nhfb
rawauthor: Tugrul Dayar and G{\"o}k{\c{c}}e N. Noyan
rawtitle: Steady-state analysis of {Google}-like stochastic matrices with block iterative methods
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.

keywords:
block iterative methods; cutsets; Google; PageRank; partitionings; power method; stochastic matrices; triangular blocks

rawabstract:
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.

entry:
@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/;
                 https://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 =     "block iterative methods; cutsets; Google; PageRank;
                 partitionings; power method; stochastic matrices;
                 triangular blocks",
}


bibtype: article
filename: pagerank.bib
label: Ding:2011:AWP
author: Ying Ding
title: Applying weighted PageRank to author citation networks
journal: j-J-AM-SOC-INF-SCI-TECHNOL
volume: 62
number: 2
pages: 236--245
month: feb
monthnumber: 2
year: 2011
CODEN: JASIEF
DOI: https://doi.org/10.1002/asi.21452
ISSN: 1532-2882 (print), 1532-2890 (electronic) OR 15322882 (print), 15322890 (electronic)
ISSNL: 1532-2882 OR 15322882
bibdate: Fri Sep 11 10:43:05 MDT 2015
bibtimestamp: 2015.09.11 10:43:05 MDT
fjournal: Journal of the American Society for Information Science and Technology: JASIST
acknowledgement: ack-nhfb
journalURL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-2890
onlinedate: 15 Nov 2010
rawauthor: Ying Ding
rawtitle: Applying weighted {PageRank} to author citation networks
bibsource:
https://www.math.utah.edu/pub/tex/bib/jasist.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Ding:2011:AWP,
  author =       "Ying Ding",
  title =        "Applying weighted {PageRank} to author citation
                 networks",
  journal =      j-J-AM-SOC-INF-SCI-TECHNOL,
  volume =       "62",
  number =       "2",
  pages =        "236--245",
  month =        feb,
  year =         "2011",
  CODEN =        "JASIEF",
  DOI =          "https://doi.org/10.1002/asi.21452",
  ISSN =         "1532-2882 (print), 1532-2890 (electronic)",
  ISSN-L =       "1532-2882",
  bibdate =      "Fri Sep 11 10:43:05 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jasist.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of the American Society for Information
                 Science and Technology: JASIST",
  journal-URL =  "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-2890",
  onlinedate =   "15 Nov 2010",
}


bibtype: article
filename: pagerank.bib
label: Ding:2011:TBP
author: Ying Ding
title: Topic-based PageRank on author cocitation networks
journal: j-J-AM-SOC-INF-SCI-TECHNOL
volume: 62
number: 3
pages: 449--466
month: mar
monthnumber: 3
year: 2011
CODEN: JASIEF
DOI: https://doi.org/10.1002/asi.21467
ISSN: 1532-2882 (print), 1532-2890 (electronic) OR 15322882 (print), 15322890 (electronic)
ISSNL: 1532-2882 OR 15322882
bibdate: Fri Sep 11 10:43:06 MDT 2015
bibtimestamp: 2015.09.11 10:43:06 MDT
fjournal: Journal of the American Society for Information Science and Technology: JASIST
acknowledgement: ack-nhfb
journalURL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-2890
onlinedate: 18 Jan 2011
rawauthor: Ying Ding
rawtitle: Topic-based {PageRank} on author cocitation networks
bibsource:
https://www.math.utah.edu/pub/tex/bib/jasist.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Ding:2011:TBP,
  author =       "Ying Ding",
  title =        "Topic-based {PageRank} on author cocitation networks",
  journal =      j-J-AM-SOC-INF-SCI-TECHNOL,
  volume =       "62",
  number =       "3",
  pages =        "449--466",
  month =        mar,
  year =         "2011",
  CODEN =        "JASIEF",
  DOI =          "https://doi.org/10.1002/asi.21467",
  ISSN =         "1532-2882 (print), 1532-2890 (electronic)",
  ISSN-L =       "1532-2882",
  bibdate =      "Fri Sep 11 10:43:06 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jasist.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of the American Society for Information
                 Science and Technology: JASIST",
  journal-URL =  "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-2890",
  onlinedate =   "18 Jan 2011",
}


bibtype: article
filename: pagerank.bib
label: Frahm:2011:UEP
author: K. M. Frahm and .B Georgeot and D. L. Shepelyansky
title: Universal emergence of PageRank
journal: j-J-PHYS-A-MATH-THEOR
volume: 44
number: 46
pages: 465101:1--465101:17
day: 18
month: nov
monthnumber: 11
year: 2011
CODEN: JPAMB5
DOI: https://doi.org/10.1088/1751-8113/44/46/465101
ISSN: 1751-8113 (print), 1751-8121 (electronic) OR 17518113 (print), 17518121 (electronic)
ISSNL: 1751-8113 OR 17518113
bibdate: Wed Aug 12 08:26:23 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.12 08:26:23 ???
URL: http://stacks.iop.org/1751-8121/44/i=46/a=465101
fjournal: Journal of Physics A: Mathematical and Theoretical
acknowledgement: ack-nhfb
journalURL: http://iopscience.iop.org/1751-8121
rawauthor: K. M. Frahm and .B Georgeot and D. L. Shepelyansky
rawtitle: Universal emergence of {PageRank}
abstract:
The PageRank algorithm enables us to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter $]0, 1 [$. Using extensive numerical simulations of large web networks, with a special accent on British University networks, we determine numerically and analytically the universal features of the PageRank vector at its emergence when 1. The whole network can be divided into a core part and a group of invariant subspaces. For 1, PageRank converges to a universal power-law distribution on the invariant subspaces whose size distribution also follows a universal power law. The convergence of PageRank at 1 is controlled by eigenvalues of the core part of the Google matrix, which are extremely close to unity, leading to large relaxation times as, for example, in spin glasses.

rawabstract:
The PageRank algorithm enables us to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter $]0, 1 [$. Using extensive numerical simulations of large web networks, with a special accent on British University networks, we determine numerically and analytically the universal features of the PageRank vector at its emergence when 1. The whole network can be divided into a core part and a group of invariant subspaces. For 1, PageRank converges to a universal power-law distribution on the invariant subspaces whose size distribution also follows a universal power law. The convergence of PageRank at 1 is controlled by eigenvalues of the core part of the Google matrix, which are extremely close to unity, leading to large relaxation times as, for example, in spin glasses.

entry:
@Article{Frahm:2011:UEP,
  author =       "K. M. Frahm and .B Georgeot and D. L. Shepelyansky",
  title =        "Universal emergence of {PageRank}",
  journal =      j-J-PHYS-A-MATH-THEOR,
  volume =       "44",
  number =       "46",
  pages =        "465101:1--465101:17",
  day =          "18",
  month =        nov,
  year =         "2011",
  CODEN =        "JPAMB5",
  DOI =          "https://doi.org/10.1088/1751-8113/44/46/465101",
  ISSN =         "1751-8113 (print), 1751-8121 (electronic)",
  ISSN-L =       "1751-8113",
  bibdate =      "Wed Aug 12 08:26:23 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://stacks.iop.org/1751-8121/44/i=46/a=465101",
  abstract =     "The PageRank algorithm enables us to rank the nodes of
                 a network through a specific eigenvector of the Google
                 matrix, using a damping parameter $]0, 1 [$. Using
                 extensive numerical simulations of large web networks,
                 with a special accent on British University networks,
                 we determine numerically and analytically the universal
                 features of the PageRank vector at its emergence when
                 1. The whole network can be divided into a core part
                 and a group of invariant subspaces. For 1, PageRank
                 converges to a universal power-law distribution on the
                 invariant subspaces whose size distribution also
                 follows a universal power law. The convergence of
                 PageRank at 1 is controlled by eigenvalues of the core
                 part of the Google matrix, which are extremely close to
                 unity, leading to large relaxation times as, for
                 example, in spin glasses.",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Physics A: Mathematical and Theoretical",
  journal-URL =  "http://iopscience.iop.org/1751-8121",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 6
year: 2011
CODEN: CACMA2
DOI: https://doi.org/10.1145/1953122.1953146
ISSN: 0001-0782 (print), 1557-7317 (electronic) OR 00010782 (print), 15577317 (electronic)
ISSNL: 0001-0782 OR 00010782
bibdate: Wed Jun 1 18:12:20 MDT 2011
bibsource: http://www.acm.org/pubs/contents/journals/cacm/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.01 18:12:20 MDT
fjournal: Communications of the ACM
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J79
rawauthor: Massimo Franceschet
rawtitle: {PageRank}: standing on the shoulders of giants
abstract:
The roots of Google's PageRank can be traced back to several early, and equally remarkable, ranking techniques.

rawabstract:
The roots of Google's PageRank can be traced back to several early, and equally remarkable, ranking techniques.

entry:
@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 =          "https://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/;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
year: 2011
CODEN: SJOCE3
DOI: https://doi.org/10.1137/110823523
ISSN: 1064-8275 (print), 1095-7197 (electronic) OR 10648275 (print), 10957197 (electronic)
ISSNL: 1064-8275 OR 10648275
bibdate: Thu Feb 9 06:05:59 MST 2012
bibtimestamp: 2012.02.09 06:05:59 MST
URL: http://epubs.siam.org/sisc/resource/1/sjoce3/v33/i6/p3201_s1
fjournal: SIAM Journal on Scientific Computing
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sisc
onlinedate: November 08, 2011
rawauthor: Chen Greif and David Kurokawa
rawtitle: A Note on the Convergence of {SOR} for the {PageRank} Problem
bibsource:
http://epubs.siam.org/sam-bin/dbq/toc/SISC/33/6; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib

entry:
@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 =          "https://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;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: Hareshkumar:2011:RWS
author: Navadiya Hareshkumar and Deepak Garg
title: Random Web Surfer PageRank Algorithm
journal: j-INT-J-COMP-APPL
volume: 35
pages: 36--41
month: dec
monthnumber: 12
year: 2011
DOI: https://doi.org/10.5120/4448-6214
ISSN: 0975-8887 OR 09758887
ISSNL: 0975-8887 OR 09758887
bibdate: Fri Jan 24 08:40:19 MST 2025
bibtimestamp: 2025.01.24 08:40:19 MST
URL: https://www.ijcaonline.org/archives/volume35/number11/4448-6214/
fjournal: International Journal of Computer Applications
acknowledgement: ack-nhfb
ajournal: Intern. J. of Computer Applications
articleno: 11
journalURL: https://www.ijcaonline.org/
rawauthor: Navadiya Hareshkumar and Deepak Garg
rawtitle: Random {Web} Surfer {PageRank} Algorithm
bibsource:
https://www.math.utah.edu/pub/tex/bib/intjcompappl.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Hareshkumar:2011:RWS,
  author =       "Navadiya Hareshkumar and Deepak Garg",
  title =        "Random {Web} Surfer {PageRank} Algorithm",
  journal =      j-INT-J-COMP-APPL,
  volume =       "35",
  number =       "??",
  pages =        "36--41",
  month =        dec,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.5120/4448-6214",
  ISSN =         "0975-8887",
  ISSN-L =       "0975-8887",
  bibdate =      "Fri Jan 24 08:40:19 MST 2025",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/intjcompappl.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://www.ijcaonline.org/archives/volume35/number11/4448-6214/",
  acknowledgement = ack-nhfb,
  ajournal =     "Intern. J. of Computer Applications",
  articleno =    "11",
  fjournal =     "International Journal of Computer Applications",
  journal-URL =  "https://www.ijcaonline.org/",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/CITA.2011.5998269
bibdate: Mon Sep 12 21:28:08 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.09.12 21:28:08 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5998269
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5984722
rawauthor: Boo Vooi Keong and Patricia Anthony
rawbooktitle: {2011 7th International Conference on Information Technology in Asia (CITA 11)}
rawtitle: {PageRank}: a modified random surfer model
entry:
@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 =          "https://doi.org/10.1109/CITA.2011.5998269",
  bibdate =      "Mon Sep 12 21:28:08 MDT 2011",
  bibsource =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 5
year: 2011
CODEN: CMPJA6
DOI: https://doi.org/10.1093/comjnl/bxq039
ISSN: 0010-4620 (print), 1460-2067 (electronic) OR 00104620 (print), 14602067 (electronic)
ISSNL: 0010-4620 OR 00104620
bibdate: Thu May 5 19:16:16 MDT 2011
bibsource: content/54/5.toc; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.05.05 19:16:16 MDT
note: See cite Croft:2010:SEI.
URL: http://comjnl.oxfordjournals.org/content/54/5/831.full.pdf+html
fjournal: The Computer Journal
keywords: Google; PageRank
acknowledgement: ack-nhfb
journalURL: http://comjnl.oxfordjournals.org/
onlinedate: April 13, 2010
rawauthor: Mark Levene
rawnote: See \cite{Croft:2010:SEI}.
rawtitle: Book Review: {Search Engines: Information Retrieval in Practice}
entry:
@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 =          "https://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;
                 https://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",
}


bibtype: book
filename: pagerank.bib
label: 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 OR 1416596585
ISBN13: 978-1-4165-9658-5 OR 9781416596585
LCCN: HD9696.8.U64 G6657 2011
bibdate: Fri Jun 3 09:45:37 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2011.06.03 09:45:37 MDT
subject: Google; Internet industry; United States
acknowledgement: ack-nhfb
rawauthor: Steven Levy
rawtitle: In the plex: how {Google} thinks, works, and shapes our lives
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.

TOC:
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

rawabstract:
Written with full cooperation from top management at Google, this is the story behind the most successful and admired technology company of our time.

rawTOC:
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

entry:
@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 =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 2
year: 2011
DOI: https://doi.org/10.1145/1921632.1921639
ISSN: 1556-4681 (print), 1556-472X (electronic) OR 15564681 (print), 1556472X (electronic)
ISSNL: 1556-4681 OR 15564681
bibdate: Mon Mar 28 11:44:01 MDT 2011
bibsource: http://www.acm.org/pubs/contents/journals/tkdd/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.03.28 11:44:01 MDT
fjournal: ACM Transactions on Knowledge Discovery from Data (TKDD)
acknowledgement: ack-nhfb
articleno: 13
rawauthor: Aditya Krishna Menon and Charles Elkan
rawtitle: Fast Algorithms for Approximating the Singular Value Decomposition
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.

rawabstract:
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.

entry:
@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 =          "https://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/;
                 https://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)",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 5
year: 2011
CODEN: JACOAH
DOI: https://doi.org/10.1145/1970392.1970397
ISSN: 0004-5411 (print), 1557-735X (electronic) OR 00045411 (print), 1557735X (electronic)
ISSNL: 0004-5411 OR 00045411
bibdate: Fri Jun 3 18:12:24 MDT 2011
bibsource: http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 18:12:24 MDT
fjournal: Journal of the ACM
acknowledgement: ack-nhfb
articleno: 13
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J401
rawauthor: Atish Das Sarma and Sreenivas Gollapudi and Rina Panigrahy
rawtitle: Estimating {PageRank} on graph streams
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.

rawabstract:
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.

entry:
@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 =          "https://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/;
                 https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/SOLI.2011.5986555
bibdate: Mon Sep 12 21:28:08 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.09.12 21:28:08 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5986555
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5975308
rawauthor: Xiaowei Shen and Xiwei Liu and Dong Fan and Changjian Cheng and Gang Xiong
rawbooktitle: {2011 IEEE International Conference on Service Operations, Logistics, and Informatics (SOLI)}
rawtitle: A performance appraisal method based on {ACP} theory and {PageRank} algorithm
entry:
@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 =          "https://doi.org/10.1109/SOLI.2011.5986555",
  bibdate =      "Mon Sep 12 21:28:08 MDT 2011",
  bibsource =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 11
year: 2011
CODEN: LAAPAW
DOI: https://doi.org/10.1016/j.laa.2011.04.018
ISSN: 0024-3795 (print), 1873-1856 (electronic) OR 00243795 (print), 18731856 (electronic)
ISSNL: 0024-3795 OR 00243795
bibdate: Mon Jun 13 18:34:49 MDT 2011
bibtimestamp: 2011.06.13 18:34:49 MDT
fjournal: Linear Algebra and its Applications
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00243795
rawauthor: Francesco Tudisco and Carmine {Di Fiore}
rawtitle: A preconditioning approach to the pagerank computation problem
bibsource:
https://www.math.utah.edu/pub/tex/bib/linala2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www.sciencedirect.com/science/journal/00243795

entry:
@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 =          "https://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 =    "https://www.math.utah.edu/pub/tex/bib/linala2010.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 8
year: 2011
DOI: https://doi.org/10.1145/2000832.2000837
ISSN: 1936-7406 (print), 1936-7414 (electronic) OR 19367406 (print), 19367414 (electronic)
ISSNL: 1936-7406 OR 19367406
bibdate: Tue Aug 30 08:13:57 MDT 2011
bibsource: http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.08.30 08:13:57 MDT
fjournal: ACM Transactions on Reconfigurable Technology and Systems (TRETS)
acknowledgement: ack-nhfb
articleno: 25
journalURL: http://portal.acm.org/toc.cfm?id=J1151
rawauthor: Jing Yan and Ning-Yi Xu and Xiong-Fei Cai and Rui Gao and Yu Wang and Rong Luo and Feng-Hsiung Hsu
rawtitle: An {FPGA}-based accelerator for {LambdaRank} in Web search engines
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.

rawabstract:
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.

entry:
@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 =          "https://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/;
                 https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/ITAP.2011.6006308
bibdate: Mon Sep 12 21:28:08 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.09.12 21:28:08 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6006308
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6005185
rawauthor: Lili Yan and Yingbin Wei and Zhanji Gui and Yizhuo Chen
rawbooktitle: {2011 International Conference on Internet Technology and Applications (iTAP)}
rawtitle: Research on {PageRank} and Hyperlink-Induced Topic Search in {Web} Structure Mining
entry:
@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 =          "https://doi.org/10.1109/ITAP.2011.6006308",
  bibdate =      "Mon Sep 12 21:28:08 MDT 2011",
  bibsource =    "https://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",
}


bibtype: inproceedings
filename: pagerank.bib
label: 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: https://doi.org/10.1109/ICMSS.2011.5999297
bibdate: Mon Sep 12 21:28:08 MDT 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.09.12 21:28:08 MDT
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5999297
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5996071
rawauthor: Peng Zha and Xiu Xu and Ming Zuo
rawbooktitle: {2011 International Conference on Management and Service Science (MASS)}
rawtitle: An Efficient Improved Strategy for the {PageRank} Algorithm
entry:
@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 =          "https://doi.org/10.1109/ICMSS.2011.5999297",
  bibdate =      "Mon Sep 12 21:28:08 MDT 2011",
  bibsource =    "https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 11
year: 2012
CODEN: AMHCBQ
DOI: https://doi.org/10.1016/j.amc.2012.08.064
ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
bibdate: Thu Oct 25 09:05:21 MDT 2012
bibtimestamp: 2012.10.25 09:05:21 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0096300312008570
fjournal: Applied Mathematics and Computation
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00963003
rawauthor: Taras Agryzkov and Jose L. Oliver and Leandro Tortosa and Jose F. Vicent
rawtitle: An algorithm for ranking the nodes of an urban network based on the concept of {PageRank} vector
bibsource:
https://www.math.utah.edu/pub/tex/bib/applmathcomput2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www.sciencedirect.com/science/journal/00963003

entry:
@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 =          "https://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 =    "https://www.math.utah.edu/pub/tex/bib/applmathcomput2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www.sciencedirect.com/science/journal/00963003",
  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",
}


bibtype: article
filename: pagerank.bib
label: Bai:2012:CIO
author: Zhong-Zhi Bai
title: On convergence of the inner--outer iteration method for computing PageRank
journal: j-NUMER-ALGEBRA-CONTROL-OPTIM
volume: 2
number: 4
pages: 855--862
year: 2012
DOI: https://doi.org/10.3934/naco.2012.2.855
ISSN: 2155-3289 (print), 2155-3297 (electronic) OR 21553289 (print), 21553297 (electronic)
ISSNL: 2155-3297 OR 21553297
bibdate: Thu Jan 31 08:21:10 MST 2019
bibsource: https://www.math.utah.edu/pub/tex/bib/naco.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2019.01.31 08:21:10 MST
URL: http://aimsciences.org/article/doi/10.3934/naco.2012.2.855
fjournal: Numerical Algebra, Control and Optimization
acknowledgement: ack-nhfb
ajournal: Numer. Algebra Control Optim.
journalURL: http://aimsciences.org/journal/2155-3289
rawauthor: Zhong-Zhi Bai
rawtitle: On convergence of the inner--outer iteration method for computing {PageRank}
entry:
@Article{Bai:2012:CIO,
  author =       "Zhong-Zhi Bai",
  title =        "On convergence of the inner--outer iteration method
                 for computing {PageRank}",
  journal =      j-NUMER-ALGEBRA-CONTROL-OPTIM,
  volume =       "2",
  number =       "4",
  pages =        "855--862",
  month =        "????",
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.3934/naco.2012.2.855",
  ISSN =         "2155-3289 (print), 2155-3297 (electronic)",
  ISSN-L =       "2155-3297",
  bibdate =      "Thu Jan 31 08:21:10 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/naco.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://aimsciences.org/article/doi/10.3934/naco.2012.2.855",
  acknowledgement = ack-nhfb,
  ajournal =     "Numer. Algebra Control Optim.",
  fjournal =     "Numerical Algebra, Control and Optimization",
  journal-URL =  "http://aimsciences.org/journal/2155-3289",
}


bibtype: article
filename: pagerank.bib
label: 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: https://doi.org/10.1007/978-3-642-30541-2_4
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
ISSNL: 0302-9743 OR 03029743
bibdate: Mon Dec 24 07:30:37 MST 2012
bibtimestamp: 2012.12.24 07:30:37 MST
fjournal: Lecture Notes in Computer Science
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1007/978-3-642-30541-2
bookURL: http://www.springerlink.com/content/978-3-642-30541-2
rawauthor: Christian Borgs and Michael Brautbar
rawtitle: A Sublinear Time Algorithm for {PageRank} Computations
bibsource:
https://www.math.utah.edu/pub/tex/bib/lncs2012e.bib; https://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

entry:
@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 =          "https://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 =    "https://www.math.utah.edu/pub/tex/bib/lncs2012e.bib;
                 https://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 =     "https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 12
year: 2012
DOI: https://doi.org/10.1016/j.comnet.2012.10.007
ISSN: 1389-1286 (print), 1872-7069 (electronic) OR 13891286 (print), 18727069 (electronic)
ISSNL: 1389-1286 OR 13891286
bibdate: Fri Nov 30 12:26:39 MST 2012
bibtimestamp: 2012.11.30 12:26:39 MST
URL: http://www.sciencedirect.com/science/article/pii/S1389128612003611
fjournal: Computer Networks
keywords: PageRank algorithm
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/13891286
rawauthor: Sergey Brin and Lawrence Page
rawtitle: Reprint of: {The anatomy of a large-scale hypertextual Web search engine}
bibsource:
https://www.math.utah.edu/pub/tex/bib/compnetamsterdam2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www.sciencedirect.com/science/journal/13891286

entry:
@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 =          "https://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 =    "https://www.math.utah.edu/pub/tex/bib/compnetamsterdam2010.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: 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: https://doi.org/10.1007/978-3-642-30541-2_11
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
ISSNL: 0302-9743 OR 03029743
bibdate: Mon Dec 24 07:30:37 MST 2012
bibtimestamp: 2012.12.24 07:30:37 MST
URL: http://link.springer.com/content/pdf/10.1007/978-3-642-30541-2_11
fjournal: Lecture Notes in Computer Science
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1007/978-3-642-30541-2
bookURL: http://www.springerlink.com/content/978-3-642-30541-2
rawauthor: Fan Chung and Paul Horn and Jacob Hughes
rawtitle: Multi-commodity Allocation for Dynamic Demands Using {PageRank} Vectors
bibsource:
https://www.math.utah.edu/pub/tex/bib/lncs2012e.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@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 =          "https://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 =    "https://www.math.utah.edu/pub/tex/bib/lncs2012e.bib;
                 https://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 =     "https://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",
}


bibtype: article
filename: pagerank.bib
label: Fiala:2012:TAP
author: Dalibor Fiala
title: Time-aware PageRank for bibliographic networks
journal: j-J-INFORMETRICS
volume: 6
number: 3
pages: 370--388
month: jul
monthnumber: 7
year: 2012
ISSN: 1751-1577 (print), 1875-5879 (electronic) OR 17511577 (print), 18755879 (electronic)
ISSNL: 1751-1577 OR 17511577
bibdate: Wed Sep 9 16:29:46 MDT 2015
bibtimestamp: 2015.09.09 16:29:46 MDT
URL: http://www.sciencedirect.com/science/article/pii/S1751157712000119
fjournal: Journal of Informetrics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/17511577/
rawauthor: Dalibor Fiala
rawtitle: Time-aware {PageRank} for bibliographic networks
bibsource:
https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Fiala:2012:TAP,
  author =       "Dalibor Fiala",
  title =        "Time-aware {PageRank} for bibliographic networks",
  journal =      j-J-INFORMETRICS,
  volume =       "6",
  number =       "3",
  pages =        "370--388",
  month =        jul,
  year =         "2012",
  CODEN =        "????",
  ISSN =         "1751-1577 (print), 1875-5879 (electronic)",
  ISSN-L =       "1751-1577",
  bibdate =      "Wed Sep 9 16:29:46 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1751157712000119",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Informetrics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/17511577/",
}


bibtype: article
filename: pagerank.bib
label: Frahm:2012:PI
author: K. M. Frahm and A. D. Chepelianskii and D. L. Shepelyansky
title: PageRank of integers
journal: j-J-PHYS-A-MATH-THEOR
volume: 45
number: 40
pages: 405101:1--405101:20
day: 12
month: oct
monthnumber: 10
year: 2012
CODEN: JPAMB5
DOI: https://doi.org/10.1088/1751-8113/45/40/405101
ISSN: 1751-8113 (print), 1751-8121 (electronic) OR 17518113 (print), 17518121 (electronic)
ISSNL: 1751-8113 OR 17518113
bibdate: Wed Aug 12 08:11:49 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.12 08:11:49 ???
URL: http://iopscience.iop.org/1751-8121/45/40/405101
fjournal: Journal of Physics A (Mathematical and General)
acknowledgement: ack-nhfb
journalURL: http://iopscience.iop.org/1751-8121
rawauthor: K. M. Frahm and A. D. Chepelianskii and D. L. Shepelyansky
rawtitle: {PageRank} of integers
entry:
@Article{Frahm:2012:PI,
  author =       "K. M. Frahm and A. D. Chepelianskii and D. L.
                 Shepelyansky",
  title =        "{PageRank} of integers",
  journal =      j-J-PHYS-A-MATH-THEOR,
  volume =       "45",
  number =       "40",
  pages =        "405101:1--405101:20",
  day =          "12",
  month =        oct,
  year =         "2012",
  CODEN =        "JPAMB5",
  DOI =          "https://doi.org/10.1088/1751-8113/45/40/405101",
  ISSN =         "1751-8113 (print), 1751-8121 (electronic)",
  ISSN-L =       "1751-8113",
  bibdate =      "Wed Aug 12 08:11:49 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://iopscience.iop.org/1751-8121/45/40/405101",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Physics A (Mathematical and General)",
  journal-URL =  "http://iopscience.iop.org/1751-8121",
}


bibtype: article
filename: pagerank.bib
label: Hudelson:2012:DPA
author: Matthew Hudelson and Barbara Logan Mooney and Aurora E. Clark
title: Determining polyhedral arrangements of atoms using PageRank
journal: j-J-MATH-CHEM
volume: 50
number: 9
pages: 2342--2350
month: oct
monthnumber: 10
year: 2012
CODEN: JMCHEG
DOI: https://doi.org/10.1007/s10910-012-0033-7
ISSN: 0259-9791 (print), 1572-8897 (electronic) OR 02599791 (print), 15728897 (electronic)
ISSNL: 0259-9791 OR 02599791
bibdate: Thu Apr 9 18:14:24 MDT 2015
bibtimestamp: 2015.04.09 18:14:24 MDT
URL: http://link.springer.com/article/10.1007/s10910-012-0033-7
fjournal: Journal of Mathematical Chemistry
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/10910
journalabr: J. Math. Chem.
rawauthor: Matthew Hudelson and Barbara Logan Mooney and Aurora E. Clark
rawtitle: Determining polyhedral arrangements of atoms using {PageRank}
bibsource:
http://link.springer.com/journal/10910/50/9; https://www.math.utah.edu/pub/tex/bib/jmathchem.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Hudelson:2012:DPA,
  author =       "Matthew Hudelson and Barbara Logan Mooney and Aurora
                 E. Clark",
  title =        "Determining polyhedral arrangements of atoms using
                 {PageRank}",
  journal =      j-J-MATH-CHEM,
  volume =       "50",
  number =       "9",
  pages =        "2342--2350",
  month =        oct,
  year =         "2012",
  CODEN =        "JMCHEG",
  DOI =          "https://doi.org/10.1007/s10910-012-0033-7",
  ISSN =         "0259-9791 (print), 1572-8897 (electronic)",
  ISSN-L =       "0259-9791",
  bibdate =      "Thu Apr 9 18:14:24 MDT 2015",
  bibsource =    "http://link.springer.com/journal/10910/50/9;
                 https://www.math.utah.edu/pub/tex/bib/jmathchem.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/article/10.1007/s10910-012-0033-7",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Mathematical Chemistry",
  journal-URL =  "http://link.springer.com/journal/10910",
  journalabr =   "J. Math. Chem.",
}


bibtype: article
filename: pagerank.bib
label: 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: https://doi.org/10.1007/978-3-642-28073-3_12
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
ISSNL: 0302-9743 OR 03029743
bibdate: Mon Dec 24 07:16:06 MST 2012
bibtimestamp: 2012.12.24 07:16:06 MST
URL: http://link.springer.com/content/pdf/10.1007/978-3-642-28073-3_12
fjournal: Lecture Notes in Computer Science
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1007/978-3-642-28073-3
bookURL: http://www.springerlink.com/content/978-3-642-28073-3
rawauthor: Tarun Kumar and Parikshit Sondhi and Ankush Mittal
rawtitle: Parallelization of {PageRank} on Multicore Processors
bibsource:
https://www.math.utah.edu/pub/tex/bib/lncs2012b.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@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 =          "https://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 =    "https://www.math.utah.edu/pub/tex/bib/lncs2012b.bib;
                 https://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 =     "https://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",
}


bibtype: book
filename: pagerank.bib
label: Langville:2012:WNO
author: Amy N. Langville and C. D. (Carl Dean) Meyer
title: Who's number one?: the science of rating and ranking
publisher: pub-PRINCETON
address: pub-PRINCETON:adr
pages: xvi + 247
year: 2012
ISBN: 0-691-15422-8 OR 0691154228
ISBN13: 978-0-691-15422-0 OR 9780691154220
LCCN: QA278.75 .L36 2012
bibdate: Tue Aug 11 17:18:26 MDT 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.loc.gov:7090/Voyager
bibtimestamp: 2015.08.11 17:18:26 MDT
subject: Ranking and selection (Statistics)
acknowledgement: ack-nhfb
rawauthor: Amy N. Langville and C. D. (Carl Dean) Meyer
rawtitle: Who's number one?: the science of rating and ranking
TOC:
Introduction to ranking Massey's method Colley's method Keener's method Elo's system The Markov method The offense-defense rating method Ranking by reordering methods Point spreads User preference ratings Handling ties Incorporating weights ``What if'' scenarios and sensitivity Rank aggregation: part 1 Rank aggregation: part 2 Methods of comparison Data Epilogue

rawTOC:
Introduction to ranking \\ Massey's method \\ Colley's method \\ Keener's method \\ Elo's system \\ The Markov method \\ The offense-defense rating method \\ Ranking by reordering methods \\ Point spreads \\ User preference ratings \\ Handling ties \\ Incorporating weights \\ ``What if'' scenarios and sensitivity \\ Rank aggregation: part 1 \\ Rank aggregation: part 2 \\ Methods of comparison \\ Data \\ Epilogue

entry:
@Book{Langville:2012:WNO,
  author =       "Amy N. Langville and C. D. (Carl Dean) Meyer",
  title =        "Who's number one?: the science of rating and ranking",
  publisher =    pub-PRINCETON,
  address =      pub-PRINCETON:adr,
  pages =        "xvi + 247",
  year =         "2012",
  ISBN =         "0-691-15422-8",
  ISBN-13 =      "978-0-691-15422-0",
  LCCN =         "QA278.75 .L36 2012",
  bibdate =      "Tue Aug 11 17:18:26 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  acknowledgement = ack-nhfb,
  subject =      "Ranking and selection (Statistics)",
  tableofcontents = "Introduction to ranking \\
                 Massey's method \\
                 Colley's method \\
                 Keener's method \\
                 Elo's system \\
                 The Markov method \\
                 The offense-defense rating method \\
                 Ranking by reordering methods \\
                 Point spreads \\
                 User preference ratings \\
                 Handling ties \\
                 Incorporating weights \\
                 ``What if'' scenarios and sensitivity \\
                 Rank aggregation: part 1 \\
                 Rank aggregation: part 2 \\
                 Methods of comparison \\
                 Data \\
                 Epilogue",
  xxtitle =      "Who's \#1?: the science of rating and ranking",
}


bibtype: article
filename: pagerank.bib
label: 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: https://doi.org/10.1007/978-3-642-31576-3_76
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
ISSNL: 0302-9743 OR 03029743
bibdate: Mon Dec 24 07:42:40 MST 2012
bibtimestamp: 2012.12.24 07:42:40 MST
URL: http://link.springer.com/content/pdf/10.1007/978-3-642-31576-3_76
fjournal: Lecture Notes in Computer Science
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1007/978-3-642-31576-3
bookURL: http://www.springerlink.com/content/978-3-642-31576-3
rawauthor: Dian-Xing Liu and Xia Yan and Wei Xie
rawtitle: Improved {PageRank} Algorithm Based on the Residence Time of the {Website}
bibsource:
https://www.math.utah.edu/pub/tex/bib/lncs2012f.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@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 =          "https://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 =    "https://www.math.utah.edu/pub/tex/bib/lncs2012f.bib;
                 https://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 =     "https://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",
}


bibtype: book
filename: pagerank.bib
label: 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 + 2 + 219
year: 2012
LCCN: QA76 .M21453 2012
bibdate: Tue May 5 17:16:06 MDT 2015
bibtimestamp: 2015.05.05 17:16:06 MDT
note: With a foreword by Christopher M. Bishop.
URL: http://press.princeton.edu/chapters/s9528.pdf; http://www.jstor.org/stable/10.2307/j.ctt7t71s
subject: Computer science; Computer algorithms; Artificial intelligence
acknowledgement: ack-nhfb
authordates: 1972--
rawauthor: John MacCormick
rawnote: With a foreword by Christopher M. Bishop.
rawtitle: Nine Algorithms That Changed the Future: the Ingenious Ideas That Drive Today's Computers
ISBN:
0-691-14714-0 (hardcover), 0-691-15819-3 (paperback) OR 0691147140 (hardcover), 0691158193 (paperback)

ISBN13:
978-0-691-14714-7 (hardcover), 978-0-691-15819-8 (paperback) OR 9780691147147 (hardcover), 9780691158198 (paperback)

bibsource:
fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; https://www.math.utah.edu/pub/tex/bib/datacompression.bib; https://www.math.utah.edu/pub/tex/bib/mathgaz2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.loc.gov:7090/Voyager

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.

remark:
The coverage of the history of PageRank algorithm in this book is deficient; see the commentary in cite Robertson:2019:BHS.

TOC:
Foreword / ix 1. Introduction: What Are the Extraordinary Ideas Computers Use Every Day? / 1 2. Search Engine Indexing: Finding Needles in the World's Biggest Haystack / 10 3. PageRank: The Technology That Launched Google / 24 4. Public Key Cryptography: Sending Secrets on a Postcard 38 5. Error-Correcting Codes: Mistakes That Fix Themselves / 60 6. Pattern Recognition: Learning from Experience / 80 7. Data Compression: Something for Nothing / 105 8. Databases: The Quest for Consistency / 122 9. Digital Signatures: Who Really Wrote This Software? / 149 10. What Is Computable? / 174 11. Conclusion: More Genius at Your Fingertips? / 199 Acknowledgments / 205 Sources and Further Reading / 207 Index / 211

rawabstract:
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.

rawTOC:
Foreword / ix \\ 1. Introduction: What Are the Extraordinary Ideas Computers Use Every Day? / 1 \\ 2. Search Engine Indexing: Finding Needles in the World's Biggest Haystack / 10 \\ 3. PageRank: The Technology That Launched Google / 24 \\ 4. Public Key Cryptography: Sending Secrets on a Postcard 38 \\ 5. Error-Correcting Codes: Mistakes That Fix Themselves / 60 \\ 6. Pattern Recognition: Learning from Experience / 80 \\ 7. Data Compression: Something for Nothing / 105 \\ 8. Databases: The Quest for Consistency / 122 \\ 9. Digital Signatures: Who Really Wrote This Software? / 149 \\ 10. What Is Computable? / 174 \\ 11. Conclusion: More Genius at Your Fingertips? / 199 \\ Acknowledgments / 205 \\ Sources and Further Reading / 207 \\ Index / 211

entry:
@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 + 2 + 219",
  year =         "2012",
  ISBN =         "0-691-14714-0 (hardcover), 0-691-15819-3 (paperback)",
  ISBN-13 =      "978-0-691-14714-7 (hardcover), 978-0-691-15819-8
                 (paperback)",
  LCCN =         "QA76 .M21453 2012",
  bibdate =      "Tue May 5 17:16:06 MDT 2015",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
                 https://www.math.utah.edu/pub/tex/bib/mathgaz2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  note =         "With a foreword by Christopher M. Bishop.",
  URL =          "http://press.princeton.edu/chapters/s9528.pdf;
                 http://www.jstor.org/stable/10.2307/j.ctt7t71s",
  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,
  author-dates = "1972--",
  remark =       "The coverage of the history of PageRank algorithm in
                 this book is deficient; see the commentary in
                 \cite{Robertson:2019:BHS}.",
  subject =      "Computer science; Computer algorithms; Artificial
                 intelligence",
  tableofcontents = "Foreword / ix \\
                 1. Introduction: What Are the Extraordinary Ideas
                 Computers Use Every Day? / 1 \\
                 2. Search Engine Indexing: Finding Needles in the
                 World's Biggest Haystack / 10 \\
                 3. PageRank: The Technology That Launched Google / 24
                 \\
                 4. Public Key Cryptography: Sending Secrets on a
                 Postcard 38 \\
                 5. Error-Correcting Codes: Mistakes That Fix Themselves
                 / 60 \\
                 6. Pattern Recognition: Learning from Experience / 80
                 \\
                 7. Data Compression: Something for Nothing / 105 \\
                 8. Databases: The Quest for Consistency / 122 \\
                 9. Digital Signatures: Who Really Wrote This Software?
                 / 149 \\
                 10. What Is Computable? / 174 \\
                 11. Conclusion: More Genius at Your Fingertips? / 199
                 \\
                 Acknowledgments / 205 \\
                 Sources and Further Reading / 207 \\
                 Index / 211",
}


bibtype: article
filename: pagerank.bib
label: Makris:2012:WQD
author: Christos Makris and Yannis Plegas and Sofia Stamou
title: Web query disambiguation using PageRank
journal: j-J-AM-SOC-INF-SCI-TECHNOL
volume: 63
number: 8
pages: 1581--1592
month: aug
monthnumber: 8
year: 2012
CODEN: JASIEF
DOI: https://doi.org/10.1002/asi.22685
ISSN: 1532-2882 (print), 1532-2890 (electronic) OR 15322882 (print), 15322890 (electronic)
ISSNL: 1532-2882 OR 15322882
bibdate: Fri Sep 11 10:43:15 MDT 2015
bibtimestamp: 2015.09.11 10:43:15 MDT
fjournal: Journal of the American Society for Information Science and Technology: JASIST
acknowledgement: ack-nhfb
journalURL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-2890
onlinedate: 29 Jun 2012
rawauthor: Christos Makris and Yannis Plegas and Sofia Stamou
rawtitle: {Web} query disambiguation using {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/jasist.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Makris:2012:WQD,
  author =       "Christos Makris and Yannis Plegas and Sofia Stamou",
  title =        "{Web} query disambiguation using {PageRank}",
  journal =      j-J-AM-SOC-INF-SCI-TECHNOL,
  volume =       "63",
  number =       "8",
  pages =        "1581--1592",
  month =        aug,
  year =         "2012",
  CODEN =        "JASIEF",
  DOI =          "https://doi.org/10.1002/asi.22685",
  ISSN =         "1532-2882 (print), 1532-2890 (electronic)",
  ISSN-L =       "1532-2882",
  bibdate =      "Fri Sep 11 10:43:15 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jasist.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of the American Society for Information
                 Science and Technology: JASIST",
  journal-URL =  "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-2890",
  onlinedate =   "29 Jun 2012",
}


bibtype: incollection
filename: pagerank.bib
label: Rebaza:2012:GPA
author: Jorge Rebaza
title: Google's PageRank Algorithm
crossref: Rebaza:2012:FCA
chapter: 2.3
pages: ??--??
year: 2012
bibdate: Tue May 12 09:32:37 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.05.12 09:32:37 ???
acknowledgement: ack-nhfb
rawauthor: Jorge Rebaza
rawtitle: {Google}'s {PageRank} Algorithm
entry:
@InCollection{Rebaza:2012:GPA,
  author =       "Jorge Rebaza",
  title =        "{Google}'s {PageRank} Algorithm",
  crossref =     "Rebaza:2012:FCA",
  chapter =      "2.3",
  pages =        "??--??",
  year =         "2012",
  bibdate =      "Tue May 12 09:32:37 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: 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: https://doi.org/10.1007/978-3-642-30541-2_10
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
ISSNL: 0302-9743 OR 03029743
bibdate: Mon Dec 24 07:30:37 MST 2012
bibtimestamp: 2012.12.24 07:30:37 MST
URL: http://link.springer.com/content/pdf/10.1007/978-3-642-30541-2_10
fjournal: Lecture Notes in Computer Science
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1007/978-3-642-30541-2
bookURL: http://www.springerlink.com/content/978-3-642-30541-2
rawauthor: Ryan A. Rossi and David F. Gleich
rawtitle: Dynamic {PageRank} Using Evolving Teleportation
bibsource:
https://www.math.utah.edu/pub/tex/bib/lncs2012e.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@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 =          "https://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 =    "https://www.math.utah.edu/pub/tex/bib/lncs2012e.bib;
                 https://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 =     "https://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",
}


bibtype: article
filename: pagerank.bib
label: Sanderson:2012:HIR
author: M. Sanderson and W. B. Croft
title: The History of Information Retrieval Research
journal: j-PROC-IEEE
volume: 100
number: Special Centennial Issue
pages: 1444--1451
month: may
monthnumber: 5
year: 2012
CODEN: IEEPAD
DOI: https://doi.org/10.1109/jproc.2012.2189916
ISSN: 0018-9219 (print), 1558-2256 (electronic) OR 00189219 (print), 15582256 (electronic)
ISSNL: 0018-9219 OR 00189219
bibdate: Mon Jul 8 08:40:26 2019
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2019.07.08 08:40:26 ???
fjournal: Proceedings of the IEEE
acknowledgement: ack-nhfb
journalURL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5
rawauthor: M. Sanderson and W. B. Croft
rawtitle: The History of Information Retrieval Research
entry:
@Article{Sanderson:2012:HIR,
  author =       "M. Sanderson and W. B. Croft",
  title =        "The History of Information Retrieval Research",
  journal =      j-PROC-IEEE,
  volume =       "100",
  number =       "Special Centennial Issue",
  pages =        "1444--1451",
  month =        may,
  year =         "2012",
  CODEN =        "IEEPAD",
  DOI =          "https://doi.org/10.1109/jproc.2012.2189916",
  ISSN =         "0018-9219 (print), 1558-2256 (electronic)",
  ISSN-L =       "0018-9219",
  bibdate =      "Mon Jul 8 08:40:26 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the IEEE",
  journal-URL =  "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5",
}


bibtype: article
filename: pagerank.bib
label: Winter:2012:GGC
journal: j-PLOS-COMPUT-BIOL
volume: 8
pages: e1002511
month: jul
monthnumber: 7
year: 2012
CODEN: PCBLBG
DOI: https://doi.org/10.1371/journal.pcbi.1002511
ISSN: 1553-734X (print), 1553-7358 (electronic) OR 1553734X (print), 15537358 (electronic)
ISSNL: 1553-734X OR 1553734X
bibdate: Tue Aug 11 17:47:14 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:47:14 ???
URL: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002511
fjournal: PLoS Computational Biology
keywords: PageRank
acknowledgement: ack-nhfb
journalURL: http://compbiol.plosjournals.org/
onlinedate: 17 May 2012
author:
Christof Winter and Glen Kristiansen and Stephan Kersting and Janine Roy and Daniela Aust and Thomas Knosel and Petra Rummele and Beatrix Jahnke and Vera Hentrich and Felix Ruckert and Marco Niedergethmann and Wilko Weichert and Marcus Bahra and Hans J. Schlitt and Utz Settmacher and Helmut Friess and Markus Buchler and Hans-Detlev Saeger and Michael Schroeder and Christian Pilarsky and Robert Grutzmann

title:
Google goes cancer: Improving outcome prediction for cancer patients by network-based ranking of marker genes

abstract:
Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

rawabstract:
Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7\%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

rawauthor:
Christof Winter and Glen Kristiansen and Stephan Kersting and Janine Roy and Daniela Aust and Thomas Kn{\"o}sel and Petra R{\"u}mmele and Beatrix Jahnke and Vera Hentrich and Felix R{\"u}ckert and Marco Niedergethmann and Wilko Weichert and Marcus Bahra and Hans J. Schlitt and Utz Settmacher and Helmut Friess and Markus B{\"u}chler and Hans-Detlev Saeger and Michael Schroeder and Christian Pilarsky and Robert Gr{\"u}tzmann

rawtitle:
{Google} goes cancer: Improving outcome prediction for cancer patients by network-based ranking of marker genes

entry:
@Article{Winter:2012:GGC,
  author =       "Christof Winter and Glen Kristiansen and Stephan
                 Kersting and Janine Roy and Daniela Aust and Thomas
                 Kn{\"o}sel and Petra R{\"u}mmele and Beatrix Jahnke and
                 Vera Hentrich and Felix R{\"u}ckert and Marco
                 Niedergethmann and Wilko Weichert and Marcus Bahra and
                 Hans J. Schlitt and Utz Settmacher and Helmut Friess
                 and Markus B{\"u}chler and Hans-Detlev Saeger and
                 Michael Schroeder and Christian Pilarsky and Robert
                 Gr{\"u}tzmann",
  title =        "{Google} goes cancer: Improving outcome prediction for
                 cancer patients by network-based ranking of marker
                 genes",
  journal =      j-PLOS-COMPUT-BIOL,
  volume =       "8",
  number =       "??",
  pages =        "e1002511",
  month =        jul,
  year =         "2012",
  CODEN =        "PCBLBG",
  DOI =          "https://doi.org/10.1371/journal.pcbi.1002511",
  ISSN =         "1553-734X (print), 1553-7358 (electronic)",
  ISSN-L =       "1553-734X",
  bibdate =      "Tue Aug 11 17:47:14 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002511",
  abstract =     "Predicting the clinical outcome of cancer patients
                 based on the expression of marker genes in their tumors
                 has received increasing interest in the past decade.
                 Accurate predictors of outcome and response to therapy
                 could be used to personalize and thereby improve
                 therapy. However, state of the art methods used so far
                 often found marker genes with limited prediction
                 accuracy, limited reproducibility, and unclear
                 biological relevance. To address this problem, we
                 developed a novel computational approach to identify
                 genes prognostic for outcome that couples gene
                 expression measurements from primary tumor samples with
                 a network of known relationships between the genes. Our
                 approach ranks genes according to their prognostic
                 relevance using both expression and network information
                 in a manner similar to Google's PageRank. We applied
                 this method to gene expression profiles which we
                 obtained from 30 patients with pancreatic cancer, and
                 identified seven candidate marker genes prognostic for
                 outcome. Compared to genes found with state of the art
                 methods, such as Pearson correlation of gene expression
                 with survival time, we improve the prediction accuracy
                 by up to 7\%. Accuracies were assessed using support
                 vector machine classifiers and Monte Carlo
                 cross-validation. We then validated the prognostic
                 value of our seven candidate markers using
                 immunohistochemistry on an independent set of 412
                 pancreatic cancer samples. Notably, signatures derived
                 from our candidate markers were independently
                 predictive of outcome and superior to established
                 clinical prognostic factors such as grade, tumor size,
                 and nodal status. As the amount of genomic data of
                 individual tumors grows rapidly, our algorithm meets
                 the need for powerful computational approaches that are
                 key to exploit these data for personalized cancer
                 therapies in clinical practice.",
  acknowledgement = ack-nhfb,
  fjournal =     "PLoS Computational Biology",
  journal-URL =  "http://compbiol.plosjournals.org/",
  keywords =     "PageRank",
  onlinedate =   "17 May 2012",
}


bibtype: article
filename: pagerank.bib
label: 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
year: 2012
CODEN: SJOCE3
DOI: https://doi.org/10.1137/110834585
ISSN: 1064-8275 (print), 1095-7197 (electronic) OR 10648275 (print), 10957197 (electronic)
ISSNL: 1064-8275 OR 10648275
bibdate: Tue Oct 30 14:49:10 MDT 2012
bibtimestamp: 2012.10.30 14:49:10 MDT
fjournal: SIAM Journal on Scientific Computing
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sisc
onlinedate: January 2012
rawauthor: Gang Wu and Yan-Chun Wang and Xiao-Qing Jin
bibsource:
http://epubs.siam.org/sam-bin/dbq/toc/SISC/34/5; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib

rawtitle:
A Preconditioned and Shifted {GMRES} Algorithm for the {PageRank} Problem with Multiple Damping Factors

entry:
@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 =          "https://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;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 1
year: 2012
CODEN: NLAAEM
DOI: https://doi.org/10.1002/nla.789
ISSN: 1070-5325 (print), 1099-1506 (electronic) OR 10705325 (print), 10991506 (electronic)
ISSNL: 1070-5325 OR 10705325
bibdate: Fri Mar 16 18:11:23 MDT 2012
bibtimestamp: 2012.03.16 18:11:23 MDT
fjournal: Numerical Linear Algebra with Applications
acknowledgement: ack-nhfb
journalURL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1506
onlinedate: 20 Nov 2011
rawauthor: Jun-Feng Yin and Guo-Jian Yin and Michael Ng
rawtitle: On adaptively accelerated {Arnoldi} method for computing {PageRank}
bibsource:
http://www.interscience.wiley.com/jpages/1070-5325; https://www.math.utah.edu/pub/tex/bib/numlinaa.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www3.interscience.wiley.com/journalfinder.html

entry:
@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 =          "https://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://www.interscience.wiley.com/jpages/1070-5325;
                 https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 http://www3.interscience.wiley.com/journalfinder.html",
  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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 2
year: 2012
DOI: https://doi.org/10.1145/2089094.2089112
ISSN: 2157-6904 (print), 2157-6912 (electronic) OR 21576904 (print), 21576912 (electronic)
ISSNL: 2157-6904 OR 21576904
bibdate: Fri Mar 16 15:10:10 MDT 2012
bibtimestamp: 2012.03.16 15:10:10 MDT
fjournal: ACM Transactions on Intelligent Systems and Technology (TIST)
acknowledgement: ack-nhfb
articleno: 36
rawauthor: Weinan Zhang and Dingquan Wang and Gui-Rong Xue and Hongyuan Zha
rawtitle: Advertising Keywords Recommendation for Short-Text {Web} Pages Using {Wikipedia}
bibsource:
http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://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.

rawabstract:
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.

entry:
@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 =          "https://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/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://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)",
}


bibtype: article
filename: pagerank.bib
label: Zhou:2012:PAC
author: Yunkai Zhou
title: Practical acceleration for computing the HITS ExpertRank vectors
journal: j-J-COMPUT-APPL-MATH
volume: 236
number: 17
pages: 4398--4409
month: nov
monthnumber: 11
year: 2012
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Sat Feb 25 13:24:36 MST 2017
bibtimestamp: 2017.02.25 13:24:36 MST
URL: http://www.sciencedirect.com/science/article/pii/S0377042712001665
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Yunkai Zhou
rawtitle: Practical acceleration for computing the {HITS} {ExpertRank} vectors
bibsource:
https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Zhou:2012:PAC,
  author =       "Yunkai Zhou",
  title =        "Practical acceleration for computing the {HITS}
                 {ExpertRank} vectors",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "236",
  number =       "17",
  pages =        "4398--4409",
  month =        nov,
  year =         "2012",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Sat Feb 25 13:24:36 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042712001665",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
}


bibtype: article
filename: pagerank.bib
label: Banky:2013:EOL
author: Daniel Banky and Gabor Ivan and Vince Grolmusz
journal: j-PLOS-ONE
volume: 8
number: 1
pages: e54204:1--e54204:7
month: jan
monthnumber: 1
year: 2013
CODEN: POLNCL
DOI: https://doi.org/10.1371/journal.pone.0054204
ISSN: 1932-6203 OR 19326203
bibdate: Wed Aug 12 08:33:35 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.12 08:33:35 ???
URL: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0054204
fjournal: PLoS One
acknowledgement: ack-nhfb
journalURL: http://www.plosone.org/
rawauthor: D{\'a}niel B{\'a}nky and G{\'a}bor Iv{\'a}n and Vince Grolmusz
title:
Equal Opportunity for Low-Degree Network Nodes: A PageRank-Based Method for Protein Target Identification in Metabolic Graphs

abstract:
Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (em Mycobacterium tuberculosis, em Plasmodium falciparum andd em MRSA Staphylococcus aureus), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the directed edge structure of the graph.

rawabstract:
Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms ({\em Mycobacterium tuberculosis}, {\em Plasmodium falciparum\/} andd {\em MRSA Staphylococcus aureus}), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the directed edge structure of the graph.

rawtitle:
Equal Opportunity for Low-Degree Network Nodes: A {PageRank}-Based Method for Protein Target Identification in Metabolic Graphs

entry:
@Article{Banky:2013:EOL,
  author =       "D{\'a}niel B{\'a}nky and G{\'a}bor Iv{\'a}n and Vince
                 Grolmusz",
  title =        "Equal Opportunity for Low-Degree Network Nodes: A
                 {PageRank}-Based Method for Protein Target
                 Identification in Metabolic Graphs",
  journal =      j-PLOS-ONE,
  volume =       "8",
  number =       "1",
  pages =        "e54204:1--e54204:7",
  month =        jan,
  year =         "2013",
  CODEN =        "POLNCL",
  DOI =          "https://doi.org/10.1371/journal.pone.0054204",
  ISSN =         "1932-6203",
  bibdate =      "Wed Aug 12 08:33:35 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0054204",
  abstract =     "Biological network data, such as metabolic-,
                 signaling- or physical interaction graphs of proteins
                 are increasingly available in public repositories for
                 important species. Tools for the quantitative analysis
                 of these networks are being developed today. Protein
                 network-based drug target identification methods
                 usually return protein hubs with large degrees in the
                 networks as potentially important targets. Some known,
                 important protein targets, however, are not hubs at
                 all, and perturbing protein hubs in these networks may
                 have several unwanted physiological effects, due to
                 their interaction with numerous partners. Here, we show
                 a novel method applicable in networks with directed
                 edges (such as metabolic networks) that compensates for
                 the low degree (non-hub) vertices in the network, and
                 identifies important nodes, regardless of their hub
                 properties. Our method computes the PageRank for the
                 nodes of the network, and divides the PageRank by the
                 in-degree (i.e., the number of incoming edges) of the
                 node. This quotient is the same in all nodes in an
                 undirected graph (even for large- and low-degree nodes,
                 that is, for hubs and non-hubs as well), but may differ
                 significantly from node to node in directed graphs. We
                 suggest to assign importance to non-hub nodes with
                 large PageRank/in-degree quotient. Consequently, our
                 method gives high scores to nodes with large PageRank,
                 relative to their degrees: therefore non-hub important
                 nodes can easily be identified in large networks. We
                 demonstrate that these relatively high PageRank scores
                 have biological relevance: the method correctly finds
                 numerous already validated drug targets in distinct
                 organisms ({\em Mycobacterium tuberculosis}, {\em
                 Plasmodium falciparum\/} andd {\em MRSA Staphylococcus
                 aureus}), and consequently, it may suggest new possible
                 protein targets as well. Additionally, our scoring
                 method was not chosen arbitrarily: its value for all
                 nodes of all undirected graphs is constant; therefore
                 its high value captures importance in the directed edge
                 structure of the graph.",
  acknowledgement = ack-nhfb,
  fjournal =     "PLoS One",
  journal-URL =  "http://www.plosone.org/",
}


bibtype: article
filename: pagerank.bib
label: Benzi:2013:CAG
author: Michele Benzi and Verena Kuhlemann
title: Chebyshev acceleration of the GeneRank algorithm
journal: j-ELECTRON-TRANS-NUMER-ANAL
volume: 40
pages: 311--320
year: 2013
ISSN: 1068-9613 (print), 1097-4067 (electronic) OR 10689613 (print), 10974067 (electronic)
ISSNL: 1068-9613 OR 10689613
bibdate: Mon Mar 31 18:49:50 MDT 2014
bibsource: https://www.math.utah.edu/pub/tex/bib/etna.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2014.03.31 18:49:50 MDT
URL: http://etna.mcs.kent.edu//vol.40.2013/pp311-320.dir/pp311-320.pdf
acknowledgement: ack-nhfb
journalURL: http://etna.mcs.kent.edu/
rawauthor: Michele Benzi and Verena Kuhlemann
rawtitle: {Chebyshev} acceleration of the {GeneRank} algorithm
entry:
@Article{Benzi:2013:CAG,
  author =       "Michele Benzi and Verena Kuhlemann",
  title =        "{Chebyshev} acceleration of the {GeneRank} algorithm",
  journal =      j-ELECTRON-TRANS-NUMER-ANAL,
  volume =       "40",
  pages =        "311--320",
  year =         "2013",
  CODEN =        "????",
  ISSN =         "1068-9613 (print), 1097-4067 (electronic)",
  ISSN-L =       "1068-9613",
  bibdate =      "Mon Mar 31 18:49:50 MDT 2014",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/etna.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://etna.mcs.kent.edu//vol.40.2013/pp311-320.dir/pp311-320.pdf",
  acknowledgement = ack-nhfb,
  journal-URL =  "http://etna.mcs.kent.edu/",
}


bibtype: article
filename: pagerank.bib
label: Garcia:2013:LPP
author: E. Garcia 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
monthnumber: 8
year: 2013
CODEN: LAAPAW
ISSN: 0024-3795 (print), 1873-1856 (electronic) OR 00243795 (print), 18731856 (electronic)
ISSNL: 0024-3795 OR 00243795
bibdate: Mon Jun 24 07:02:58 MDT 2013
bibtimestamp: 2013.06.24 07:02:58 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0024379512007835
fjournal: Linear Algebra and its Applications
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00243795
rawauthor: E. Garc{\'\i}a and F. Pedroche and M. Romance
rawtitle: On the localization of the personalized {PageRank} of complex networks
bibsource:
https://www.math.utah.edu/pub/tex/bib/linala2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www.sciencedirect.com/science/journal/00243795

entry:
@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 =    "https://www.math.utah.edu/pub/tex/bib/linala2010.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: Halu:2013:MP
author: Arda Halu and Raul J. Mondragon and Pietro Panzarasa and Ginestra Bianconi
title: Multiplex PageRank
journal: j-PLOS-ONE
volume: 8
pages: e78293:1--e78293:10
year: 2013
CODEN: POLNCL
DOI: https://doi.org/10.1371/journal.pone.0078293
ISSN: 1932-6203 OR 19326203
bibdate: Tue Aug 11 17:02:55 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:02:55 ???
URL: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0078293
fjournal: PLoS One
acknowledgement: ack-nhfb
journalURL: http://www.plosone.org/
onlinedate: 30 October 2013
rawauthor: Arda Halu and Ra{\'u}l J. Mondrag{\'o}n and Pietro Panzarasa and Ginestra Bianconi
rawtitle: Multiplex {PageRank}
abstract:
Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.

rawabstract:
Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.

entry:
@Article{Halu:2013:MP,
  author =       "Arda Halu and Ra{\'u}l J. Mondrag{\'o}n and Pietro
                 Panzarasa and Ginestra Bianconi",
  title =        "Multiplex {PageRank}",
  journal =      j-PLOS-ONE,
  volume =       "8",
  number =       "??",
  pages =        "e78293:1--e78293:10",
  month =        "????",
  year =         "2013",
  CODEN =        "POLNCL",
  DOI =          "https://doi.org/10.1371/journal.pone.0078293",
  ISSN =         "1932-6203",
  bibdate =      "Tue Aug 11 17:02:55 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0078293",
  abstract =     "Many complex systems can be described as multiplex
                 networks in which the same nodes can interact with one
                 another in different layers, thus forming a set of
                 interacting and co-evolving networks. Examples of such
                 multiplex systems are social networks where people are
                 involved in different types of relationships and
                 interact through various forms of communication media.
                 The ranking of nodes in multiplex networks is one of
                 the most pressing and challenging tasks that research
                 on complex networks is currently facing. When pairs of
                 nodes can be connected through multiple links and in
                 multiple layers, the ranking of nodes should
                 necessarily reflect the importance of nodes in one
                 layer as well as their importance in other
                 interdependent layers. In this paper, we draw on the
                 idea of biased random walks to define the Multiplex
                 PageRank centrality measure in which the effects of the
                 interplay between networks on the centrality of nodes
                 are directly taken into account. In particular,
                 depending on the intensity of the interaction between
                 layers, we define the Additive, Multiplicative,
                 Combined, and Neutral versions of Multiplex PageRank,
                 and show how each version reflects the extent to which
                 the importance of a node in one layer affects the
                 importance the node can gain in another layer. We
                 discuss these measures and apply them to an online
                 multiplex social network. Findings indicate that taking
                 the multiplex nature of the network into account helps
                 uncover the emergence of rankings of nodes that differ
                 from the rankings obtained from one single layer.
                 Results provide support in favor of the salience of
                 multiplex centrality measures, like Multiplex PageRank,
                 for assessing the prominence of nodes embedded in
                 multiple interacting networks, and for shedding a new
                 light on structural properties that would otherwise
                 remain undetected if each of the interacting networks
                 were analyzed in isolation.",
  acknowledgement = ack-nhfb,
  fjournal =     "PLoS One",
  journal-URL =  "http://www.plosone.org/",
  onlinedate =   "30 October 2013",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 10
year: 2013
CODEN: ATSMER
DOI: https://doi.org/10.1145/2522920.2522930
ISSN: 1049-331X (print), 1557-7392 (electronic) OR 1049331X (print), 15577392 (electronic)
ISSNL: 1049-331X OR 1049331X
bibdate: Wed Oct 30 12:18:03 MDT 2013
bibtimestamp: 2013.10.30 12:18:03 MDT
fjournal: ACM Transactions on Software Engineering and Methodology
keywords: PageRank algorithm
acknowledgement: ack-nhfb
articleno: 37
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J790
rawauthor: Collin Mcmillan and Denys Poshyvanyk and Mark Grechanik and Qing Xie and Chen Fu
rawtitle: {Portfolio}: Searching for relevant functions and their usages in millions of lines of code
bibsource:
http://www.acm.org/pubs/contents/journals/tosem/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://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.

rawabstract:
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.

entry:
@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 =          "https://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/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: Onizuka:2013:OIQ
author: Makoto Onizuka and Hiroyuki Kato and Soichiro Hidaka and Keisuke Nakano and Zhenjiang Hu
title: Optimization for iterative queries on MapReduce
journal: j-PROC-VLDB-ENDOWMENT
volume: 7
number: 4
pages: 241--252
month: dec
monthnumber: 12
year: 2013
ISSN: 2150-8097 OR 21508097
bibdate: Wed Feb 4 09:22:02 MST 2015
bibtimestamp: 2015.02.04 09:22:02 MST
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/citation.cfm?id=J1174
rawauthor: Makoto Onizuka and Hiroyuki Kato and Soichiro Hidaka and Keisuke Nakano and Zhenjiang Hu
rawtitle: Optimization for iterative queries on {MapReduce}
bibsource:
http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbe.bib

abstract:
We propose OptIQ, a query optimization approach for iterative queries in distributed environment. OptIQ removes redundant computations among different iterations by extending the traditional techniques of view materialization and incremental view evaluation. First, OptIQ decomposes iterative queries into invariant and variant views, and materializes the former view. Redundant computations are removed by reusing the materialized view among iterations. Second, OptIQ incrementally evaluates the variant view, so that redundant computations are removed by skipping the evaluation on converged tuples in the variant view. We verify the effectiveness of OptIQ through the queries of PageRank and $k$-means clustering on real datasets. The results show that OptIQ achieves high efficiency, up to five times faster than is possible without removing the redundant computations among iterations.

rawabstract:
We propose OptIQ, a query optimization approach for iterative queries in distributed environment. OptIQ removes redundant computations among different iterations by extending the traditional techniques of view materialization and incremental view evaluation. First, OptIQ decomposes iterative queries into invariant and variant views, and materializes the former view. Redundant computations are removed by reusing the materialized view among iterations. Second, OptIQ incrementally evaluates the variant view, so that redundant computations are removed by skipping the evaluation on converged tuples in the variant view. We verify the effectiveness of OptIQ through the queries of PageRank and $k$-means clustering on real datasets. The results show that OptIQ achieves high efficiency, up to five times faster than is possible without removing the redundant computations among iterations.

entry:
@Article{Onizuka:2013:OIQ,
  author =       "Makoto Onizuka and Hiroyuki Kato and Soichiro Hidaka
                 and Keisuke Nakano and Zhenjiang Hu",
  title =        "Optimization for iterative queries on {MapReduce}",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "7",
  number =       "4",
  pages =        "241--252",
  month =        dec,
  year =         "2013",
  CODEN =        "????",
  ISSN =         "2150-8097",
  bibdate =      "Wed Feb 4 09:22:02 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  abstract =     "We propose OptIQ, a query optimization approach for
                 iterative queries in distributed environment. OptIQ
                 removes redundant computations among different
                 iterations by extending the traditional techniques of
                 view materialization and incremental view evaluation.
                 First, OptIQ decomposes iterative queries into
                 invariant and variant views, and materializes the
                 former view. Redundant computations are removed by
                 reusing the materialized view among iterations. Second,
                 OptIQ incrementally evaluates the variant view, so that
                 redundant computations are removed by skipping the
                 evaluation on converged tuples in the variant view. We
                 verify the effectiveness of OptIQ through the queries
                 of PageRank and $k$-means clustering on real datasets.
                 The results show that OptIQ achieves high efficiency,
                 up to five times faster than is possible without
                 removing the redundant computations among iterations.",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "http://portal.acm.org/citation.cfm?id=J1174",
}


bibtype: inproceedings
filename: pagerank.bib
label: Wang:2013:PPP
author: William Yang Wang and Kathryn Mazaitis and William W. Cohen
editor: Qi He
title: Programming with personalized PageRank: A locally groundable first-order probabilistic logic
publisher: pub-ACM
address: pub-ACM:adr
pages: 2129--2138
year: 2013
DOI: https://doi.org/10.1145/2505515.2505573
ISBN: 1-4503-2263-8 OR 1450322638
ISBN13: 978-1-4503-2263-8 OR 9781450322638
LCCN: QA76.9.D3
bibdate: Tue Aug 11 17:44:13 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:44:13 ???
acknowledgement: ack-nhfb
bookpages: 2574
rawauthor: William Yang Wang and Kathryn Mazaitis and William W. Cohen
raweditor: Qi He
rawtitle: Programming with personalized {PageRank}: A locally groundable first-order probabilistic logic
booktitle:
CIKM'13: proceedings of the 22nd ACM International Conference on Information and Knowledge Management: Oct. 27--Nov. 1, 2013, San Francisco, CA, USA

rawbooktitle:
{CIKM'13: proceedings of the 22nd ACM International Conference on Information and Knowledge Management: Oct. 27--Nov. 1, 2013, San Francisco, CA, USA}

entry:
@InProceedings{Wang:2013:PPP,
  author =       "William Yang Wang and Kathryn Mazaitis and William W.
                 Cohen",
  editor =       "Qi He",
  booktitle =    "{CIKM'13: proceedings of the 22nd ACM International
                 Conference on Information and Knowledge Management:
                 Oct. 27--Nov. 1, 2013, San Francisco, CA, USA}",
  title =        "Programming with personalized {PageRank}: A locally
                 groundable first-order probabilistic logic",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "2129--2138",
  year =         "2013",
  DOI =          "https://doi.org/10.1145/2505515.2505573",
  ISBN =         "1-4503-2263-8",
  ISBN-13 =      "978-1-4503-2263-8",
  LCCN =         "QA76.9.D3",
  bibdate =      "Tue Aug 11 17:44:13 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  bookpages =    "2574",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 10
year: 2013
CODEN: JSCOEB
DOI: https://doi.org/10.1007/s10915-013-9696-x
ISSN: 0885-7474 (print), 1573-7691 (electronic) OR 08857474 (print), 15737691 (electronic)
ISSNL: 0885-7474 OR 08857474
bibdate: Sat Mar 8 11:16:24 MST 2014
bibtimestamp: 2014.03.08 11:16:24 MST
fjournal: Journal of Scientific Computing
acknowledgement: ack-nhfb
rawauthor: Gang Wu and Ying Zhang and Yimin Wei
rawtitle: Accelerating the {Arnoldi}-Type Algorithm for the {PageRank} Problem and the {ProteinRank} Problem
bibsource:
http://link.springer.com/journal/10915; http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0885-7474&volume=57&issue=1; https://www.math.utah.edu/pub/tex/bib/jscicomput.bib; https://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

entry:
@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 =          "https://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;
                 https://www.math.utah.edu/pub/tex/bib/jscicomput.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 8
year: 2013
DOI: https://doi.org/10.1145/2490860
ISSN: 2158-656X (print), 2158-6578 (electronic) OR 2158656X (print), 21586578 (electronic)
ISSNL: 2158-656X OR 2158656X
bibdate: Thu Mar 13 06:54:56 MDT 2014
bibtimestamp: 2014.03.13 06:54:56 MDT
fjournal: ACM Transactions on Management Information Systems (TMIS)
acknowledgement: ack-nhfb
articleno: 8
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J1320
rawauthor: Zhu Zhang and Daniel D. Zeng and Ahmed Abbasi and Jing Peng and Xiaolong Zheng
rawtitle: A Random Walk Model for Item Recommendation in Social Tagging Systems
bibsource:
http://www.acm.org/pubs/contents/journals/tmis/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://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.

rawabstract:
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.

entry:
@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 =          "https://doi.org/10.1145/2490860",
  ISSN =         "2158-656X (print), 2158-6578 (electronic)",
  ISSN-L =       "2158-656X",
  bibdate =      "Thu Mar 13 06:54:56 MDT 2014",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tmis/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 4
year: 2013
ISSN: 2150-8097 OR 21508097
bibdate: Fri Dec 13 05:56:32 MST 2013
bibtimestamp: 2013.12.13 05:56:32 MST
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
rawauthor: Fanwei Zhu and Yuan Fang and Kevin Chen-Chuan Chang and Jing Ying
rawtitle: Incremental and accuracy-aware {Personalized PageRank} through scheduled approximation
bibsource:
http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://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.

rawabstract:
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.

entry:
@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/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: Amodio:2014:RAB
author: Pierluigi Amodio and Luigi Brugnano
title: Recent advances in bibliometric indexes and the PaperRank problem
journal: j-J-COMPUT-APPL-MATH
volume: 267
pages: 182--194
month: sep
monthnumber: 9
year: 2014
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Sat Feb 25 13:34:44 MST 2017
bibtimestamp: 2017.02.25 13:34:44 MST
URL: http://www.sciencedirect.com/science/article/pii/S0377042714001046
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Pierluigi Amodio and Luigi Brugnano
rawtitle: Recent advances in bibliometric indexes and the {PaperRank} problem
entry:
@Article{Amodio:2014:RAB,
  author =       "Pierluigi Amodio and Luigi Brugnano",
  title =        "Recent advances in bibliometric indexes and the
                 {PaperRank} problem",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "267",
  number =       "??",
  pages =        "182--194",
  month =        sep,
  year =         "2014",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Sat Feb 25 13:34:44 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042714001046",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
}


bibtype: article
filename: pagerank.bib
label: Arnal:2014:PRE
author: Josep Arnal and Hector Migallon and Violeta Migallon
title: Parallel relaxed and extrapolated algorithms for computing PageRank
journal: j-J-SUPERCOMPUTING
volume: 70
number: 2
pages: 637--648
month: nov
monthnumber: 11
year: 2014
CODEN: JOSUED
DOI: https://doi.org/10.1007/s11227-014-1118-9
ISSN: 0920-8542 (print), 1573-0484 (electronic) OR 09208542 (print), 15730484 (electronic)
ISSNL: 0920-8542 OR 09208542
bibdate: Fri Feb 13 12:32:19 MST 2015
bibtimestamp: 2015.02.13 12:32:19 MST
URL: http://link.springer.com/article/10.1007/s11227-014-1118-9
fjournal: The Journal of Supercomputing
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11227
rawauthor: Josep Arnal and H{\'e}ctor Migall{\'o}n and Violeta Migall{\'o}n
rawtitle: Parallel relaxed and extrapolated algorithms for computing {PageRank}
bibsource:
http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0920-8542&volume=70&issue=2; https://www.math.utah.edu/pub/tex/bib/jsuper.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Arnal:2014:PRE,
  author =       "Josep Arnal and H{\'e}ctor Migall{\'o}n and Violeta
                 Migall{\'o}n",
  title =        "Parallel relaxed and extrapolated algorithms for
                 computing {PageRank}",
  journal =      j-J-SUPERCOMPUTING,
  volume =       "70",
  number =       "2",
  pages =        "637--648",
  month =        nov,
  year =         "2014",
  CODEN =        "JOSUED",
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  ISSN =         "0920-8542 (print), 1573-0484 (electronic)",
  ISSN-L =       "0920-8542",
  bibdate =      "Fri Feb 13 12:32:19 MST 2015",
  bibsource =    "http://springerlink.metapress.com/openurl.asp?genre=issue&issn=0920-8542&volume=70&issue=2;
                 https://www.math.utah.edu/pub/tex/bib/jsuper.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/article/10.1007/s11227-014-1118-9",
  acknowledgement = ack-nhfb,
  fjournal =     "The Journal of Supercomputing",
  journal-URL =  "http://link.springer.com/journal/11227",
}


bibtype: article
filename: pagerank.bib
label: Cheang:2014:MAE
author: Brenda Cheang and Samuel Kai Wah Chu and Chongshou Li and Andrew Lim
journal: j-J-ASSOC-INF-SCI-TECHNOL
volume: 65
number: 12
pages: 2581--2591
month: dec
monthnumber: 12
year: 2014
DOI: https://doi.org/10.1002/asi.23133
ISSN: 2330-1643 (print), 2330-1643 (electronic) OR 23301643 (print), 23301643 (electronic)
ISSNL: 2330-1643 OR 23301643
bibdate: Fri Sep 11 12:15:16 MDT 2015
bibtimestamp: 2015.09.11 12:15:16 MDT
fjournal: Journal of the Association for Information Science and Technology
acknowledgement: ack-nhfb
journalURL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2330-1643
onlinedate: 2 May 2014
rawauthor: Brenda Cheang and Samuel Kai Wah Chu and Chongshou Li and Andrew Lim
title:
A multidimensional approach to evaluating management journals: Refining PageRank via the differentiation of citation types and identifying the roles that management journals play

rawtitle:
A multidimensional approach to evaluating management journals: {Refining} {PageRank} via the differentiation of citation types and identifying the roles that management journals play

entry:
@Article{Cheang:2014:MAE,
  author =       "Brenda Cheang and Samuel Kai Wah Chu and Chongshou Li
                 and Andrew Lim",
  title =        "A multidimensional approach to evaluating management
                 journals: {Refining} {PageRank} via the differentiation
                 of citation types and identifying the roles that
                 management journals play",
  journal =      j-J-ASSOC-INF-SCI-TECHNOL,
  volume =       "65",
  number =       "12",
  pages =        "2581--2591",
  month =        dec,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1002/asi.23133",
  ISSN =         "2330-1643 (print), 2330-1643 (electronic)",
  ISSN-L =       "2330-1643",
  bibdate =      "Fri Sep 11 12:15:16 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jasist.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of the Association for Information Science and
                 Technology",
  journal-URL =  "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2330-1643",
  onlinedate =   "2 May 2014",
}


bibtype: article
filename: pagerank.bib
label: Cheang:2014:OMJ
author: Brenda Cheang and Samuel Kai Wah Chu and Chongshou Li and Andrew Lim
journal: j-SCIENTOMETRICS
volume: 100
number: 2
pages: 339--361
month: aug
monthnumber: 8
year: 2014
CODEN: SCNTDX
DOI: https://doi.org/10.1007/s11192-014-1272-0
ISSN: 0138-9130 (print), 1588-2861 (electronic) OR 01389130 (print), 15882861 (electronic)
ISSNL: 0138-9130 OR 01389130
bibdate: Wed Sep 2 12:06:03 MDT 2015
bibtimestamp: 2015.09.02 12:06:03 MDT
URL: http://link.springer.com/article/10.1007/s11192-014-1272-0
fjournal: Scientometrics
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11192
rawauthor: Brenda Cheang and Samuel Kai Wah Chu and Chongshou Li and Andrew Lim
title:
OR / MS journals evaluation based on a refined PageRank method: an updated and more comprehensive review

bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib

rawtitle:
{OR\slash MS} journals evaluation based on a refined {PageRank} method: an updated and more comprehensive review

entry:
@Article{Cheang:2014:OMJ,
  author =       "Brenda Cheang and Samuel Kai Wah Chu and Chongshou Li
                 and Andrew Lim",
  title =        "{OR\slash MS} journals evaluation based on a refined
                 {PageRank} method: an updated and more comprehensive
                 review",
  journal =      j-SCIENTOMETRICS,
  volume =       "100",
  number =       "2",
  pages =        "339--361",
  month =        aug,
  year =         "2014",
  CODEN =        "SCNTDX",
  DOI =          "https://doi.org/10.1007/s11192-014-1272-0",
  ISSN =         "0138-9130 (print), 1588-2861 (electronic)",
  ISSN-L =       "0138-9130",
  bibdate =      "Wed Sep 2 12:06:03 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib",
  URL =          "http://link.springer.com/article/10.1007/s11192-014-1272-0",
  acknowledgement = ack-nhfb,
  fjournal =     "Scientometrics",
  journal-URL =  "http://link.springer.com/journal/11192",
}


bibtype: book
filename: pagerank.bib
label: Ding:2014:MSI
author: Ying Ding
title: Measuring Scholarly Impact: Methods and Practice
publisher: pub-SV
address: pub-SV:adr
pages: xiv + 346
year: 2014
DOI: https://doi.org/10.1007/978-3-319-10377-8
ISBN: 3-319-10376-8 (paperback), 3-319-10377-6 (e-book) OR 3319103768 (paperback), 3319103776 (ebook)
LCCN: Z669.8 .M43 2014
bibdate: Wed Feb 22 14:33:58 MST 2017
bibtimestamp: 2017.02.22 14:33:58 MST
acknowledgement: ack-nhfb
rawauthor: Ying Ding
rawtitle: Measuring Scholarly Impact: Methods and Practice
ISBN13:
978-3-319-10376-1 (paperback), 978-3-319-10377-8 (e-book) OR 9783319103761 (paperback), 9783319103778 (ebook)

bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib; z3950.loc.gov:7090/Voyager

URL:
http://www.loc.gov/catdir/enhancements/fy1501/2014950682-d.html; http://www.loc.gov/catdir/enhancements/fy1501/2014950682-t.html

TOC:
Intro Preface Network Tools and Analysis The Science System Statistical and Text-Based Methods Visualization References Contents Part I: Network Tools and Analysis Chapter 1: Community Detection and Visualization of Networks with the Map Equation Framework 1.1 Introduction 1.2 Overview of Methods 1.3 The Map Equation Framework 1.4 Step-by-Step Instructions to the MapEquation Software Package References Chapter 2: Link Prediction 2.1 Introduction 2.2 The Link Prediction Process and Its Applications 2.3 Data 2.4 The Linkpred Tool 2.5 Link Prediction in Practice Appendix: Usage as a Python Module References Chapter 3: Network Analysis and Indicators 3.1 Introduction 3.2 Networks and Bibliometrics 3.3 Basic Network Properties 3.4 Network Data 3.5 Scientometrics Through Networks 3.6 Collaboration Networks 3.7 Citation Networks References Chapter 4: PageRank-Related Methods for Analyzing Citation Networks 4.1 Introduction 4.2 PageRank 4.3 Literature Review 4.4 Tutorial References Part II: The Science System Chapter 5: Systems Life Cycle and Its Relation with the Triple Helix 5.1 Introduction and Motivation 5.2 Background Work Related to This Study 5.3 Hypothesis to Test 5.4 Measurable States During the Life Cycle of a Technology 5.5 Step-by-Step Use of a Tool to Generate Results 5.6 Expansion/Evolution of Milestone 5 Concerning Technology Readiness Levels 5.7 Application of TRL Logic to the Modified Model 5.8 Discussion References Chapter 6: Spatial Scientometrics and Scholarly Impact: A Review of Recent Studies, Tools, and Methods 6.1 Introduction 6.2 Selection of Reviewed Papers 6.3 Review References Chapter 7: Researchers' Publication Patterns and Their Use for Author Disambiguation 7.1 Introduction 7.2 Previous Studies on the Attribution of Individual Authors' Publications 7.3 Methods 7.4 Regularities in Researchers' Publication Patterns Appendix 1: List of Disciplines Assigned to Journals Appendix 2: List of Disciplines Assigned to Departments References Chapter 8: Knowledge Integration and Diffusion: Measures and Mapping of Diversity and Coherence 8.1 Introduction 8.2 Conceptual Framework: Knowledge Integration and Diffusion as Shifts in Cognitive Diversity and Coherence 8.3 Choices on Data and Methods for Operationalisation 8.4 How to Compute and Visualise Knowledge Integration References Part III: Statistical and Text-Based Methods Chapter 9: Limited Dependent Variable Models and Probabilistic Prediction in Informetrics 9.1 Introduction 9.2 The Data: Which Articles Get Cited in Informetrics? 9.3 Binary Regression 9.4 Ordinal Regression 9.5 Count Data Models 9.6 Limited Dependent Variable Models in Stata References Chapter 10: Text Mining with the Stanford CoreNLP 10.1 Introduction 10.2 Text Mining in Bibliometric Research 10.3 Text Mining System Architecture 10.4 The Stanford CoreNLP Parser 10.5 An Example of Text Mining for Bibliometric Analysis 10.6 Results References Chapter 11: Topic Modeling: Measuring Scholarly Impact Using a Topical Lens 11.1 Introduction 11.2 Topic Models 11.3 Applying Topic Modeling Methods in Scholarly Communication 11.4 Topic Modeling Tool: Case Study Appendix: Normalization, Mapping, and Clustering Techniques Used by VOSviewer References Chapter 12: The Substantive and Practical Significance of Citation Impact Differences Between Institutions: Guidelines for the ldots 12.1 Introduction 12.2 Percentile Rankings 12.3 Data and Statistical Software 12.4 Effect Sizes and related concepts 12.5 Cohen's d (for Individual Institutions) 12.6 Mean Differences Between Institutions 12.7 Proportions (Both for One Institution and for Comparisons Across Institutions) Appendix: Stata Code Used for These Analyses References Part IV: Visualization Chapter 13: Visualizing Bibliometric Networks 13.1 Introduction 13.2 Literature Review 13.3 Software Tools 13.4 Techniques 13.5 Tutorials Appendix: Normalization, Mapping, and Clustering Techniques Used by VOSviewer References Chapter 14: Replicable Science of Science Studies 14.1 Open Tools for Science of Science Studies 14.2 The Science of Science (Sci2) Tool 14.3 Career Trajectories 14.4 Discussion and Outlook References Index

rawTOC:
Intro \\ Preface \\ Network Tools and Analysis \\ The Science System \\ Statistical and Text-Based Methods \\ Visualization \\ References \\ Contents \\ Part I: Network Tools and Analysis \\ Chapter 1: Community Detection and Visualization of Networks with the Map Equation Framework \\ 1.1 Introduction \\ 1.2 Overview of Methods \\ 1.3 The Map Equation Framework \\ 1.4 Step-by-Step Instructions to the MapEquation Software Package \\ References \\ Chapter 2: Link Prediction \\ 2.1 Introduction \\ 2.2 The Link Prediction Process and Its Applications \\ 2.3 Data \\ 2.4 The Linkpred Tool \\ 2.5 Link Prediction in Practice \\ Appendix: Usage as a Python Module \\ References \\ Chapter 3: Network Analysis and Indicators \\ 3.1 Introduction \\ 3.2 Networks and Bibliometrics \\ 3.3 Basic Network Properties \\ 3.4 Network Data \\ 3.5 Scientometrics Through Networks \\ 3.6 Collaboration Networks \\ 3.7 Citation Networks \\ References \\ Chapter 4: PageRank-Related Methods for Analyzing Citation Networks \\ 4.1 Introduction \\ 4.2 PageRank \\ 4.3 Literature Review \\ 4.4 Tutorial \\ References \\ Part II: The Science System \\ Chapter 5: Systems Life Cycle and Its Relation with the Triple Helix \\ 5.1 Introduction and Motivation \\ 5.2 Background Work Related to This Study \\ 5.3 Hypothesis to Test \\ 5.4 Measurable States During the Life Cycle of a Technology \\ 5.5 Step-by-Step Use of a Tool to Generate Results \\ 5.6 Expansion/Evolution of Milestone 5 Concerning Technology Readiness Levels \\ 5.7 Application of TRL Logic to the Modified Model \\ 5.8 Discussion \\ References \\ Chapter 6: Spatial Scientometrics and Scholarly Impact: A Review of Recent Studies, Tools, and Methods \\ 6.1 Introduction \\ 6.2 Selection of Reviewed Papers \\ 6.3 Review \\ References \\ Chapter 7: Researchers' Publication Patterns and Their Use for Author Disambiguation \\ 7.1 Introduction \\ 7.2 Previous Studies on the Attribution of Individual Authors' Publications \\ 7.3 Methods \\ 7.4 Regularities in Researchers' Publication Patterns \\ Appendix 1: List of Disciplines Assigned to Journals \\ Appendix 2: List of Disciplines Assigned to Departments \\ References \\ Chapter 8: Knowledge Integration and Diffusion: Measures and Mapping of Diversity and Coherence \\ 8.1 Introduction \\ 8.2 Conceptual Framework: Knowledge Integration and Diffusion as Shifts in Cognitive Diversity and Coherence \\ 8.3 Choices on Data and Methods for Operationalisation \\ 8.4 How to Compute and Visualise Knowledge Integration \\ References \\ Part III: Statistical and Text-Based Methods \\ Chapter 9: Limited Dependent Variable Models and Probabilistic Prediction in Informetrics \\ 9.1 Introduction \\ 9.2 The Data: Which Articles Get Cited in Informetrics? \\ 9.3 Binary Regression \\ 9.4 Ordinal Regression \\ 9.5 Count Data Models \\ 9.6 Limited Dependent Variable Models in Stata \\ References \\ Chapter 10: Text Mining with the Stanford CoreNLP \\ 10.1 Introduction \\ 10.2 Text Mining in Bibliometric Research \\ 10.3 Text Mining System Architecture \\ 10.4 The Stanford CoreNLP Parser \\ 10.5 An Example of Text Mining for Bibliometric Analysis \\ 10.6 Results \\ References \\ Chapter 11: Topic Modeling: Measuring Scholarly Impact Using a Topical Lens \\ 11.1 Introduction \\ 11.2 Topic Models \\ 11.3 Applying Topic Modeling Methods in Scholarly Communication \\ 11.4 Topic Modeling Tool: Case Study \\ Appendix: Normalization, Mapping, and Clustering Techniques Used by VOSviewer \\ References \\ Chapter 12: The Substantive and Practical Significance of Citation Impact Differences Between Institutions: Guidelines for the \ldots{} \\ 12.1 Introduction \\ 12.2 Percentile Rankings \\ 12.3 Data and Statistical Software \\ 12.4 Effect Sizes and related concepts \\ 12.5 Cohen's d (for Individual Institutions) \\ 12.6 Mean Differences Between Institutions \\ 12.7 Proportions (Both for One Institution and for Comparisons Across Institutions) \\ Appendix: Stata Code Used for These Analyses \\ References \\ Part IV: Visualization \\ Chapter 13: Visualizing Bibliometric Networks \\ 13.1 Introduction \\ 13.2 Literature Review \\ 13.3 Software Tools \\ 13.4 Techniques \\ 13.5 Tutorials \\ Appendix: Normalization, Mapping, and Clustering Techniques Used by VOSviewer \\ References \\ Chapter 14: Replicable Science of Science Studies \\ 14.1 Open Tools for Science of Science Studies \\ 14.2 The Science of Science (Sci2) Tool \\ 14.3 Career Trajectories \\ 14.4 Discussion and Outlook \\ References \\ Index

entry:
@Book{Ding:2014:MSI,
  author =       "Ying Ding",
  title =        "Measuring Scholarly Impact: Methods and Practice",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "xiv + 346",
  year =         "2014",
  DOI =          "https://doi.org/10.1007/978-3-319-10377-8",
  ISBN =         "3-319-10376-8 (paperback), 3-319-10377-6 (e-book)",
  ISBN-13 =      "978-3-319-10376-1 (paperback), 978-3-319-10377-8
                 (e-book)",
  LCCN =         "Z669.8 .M43 2014",
  bibdate =      "Wed Feb 22 14:33:58 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib;
                 z3950.loc.gov:7090/Voyager",
  URL =          "http://www.loc.gov/catdir/enhancements/fy1501/2014950682-d.html;
                 http://www.loc.gov/catdir/enhancements/fy1501/2014950682-t.html",
  acknowledgement = ack-nhfb,
  tableofcontents = "Intro \\
                 Preface \\
                 Network Tools and Analysis \\
                 The Science System \\
                 Statistical and Text-Based Methods \\
                 Visualization \\
                 References \\
                 Contents \\
                 Part I: Network Tools and Analysis \\
                 Chapter 1: Community Detection and Visualization of
                 Networks with the Map Equation Framework \\
                 1.1 Introduction \\
                 1.2 Overview of Methods \\
                 1.3 The Map Equation Framework \\
                 1.4 Step-by-Step Instructions to the MapEquation
                 Software Package \\
                 References \\
                 Chapter 2: Link Prediction \\
                 2.1 Introduction \\
                 2.2 The Link Prediction Process and Its Applications
                 \\
                 2.3 Data \\
                 2.4 The Linkpred Tool \\
                 2.5 Link Prediction in Practice \\
                 Appendix: Usage as a Python Module \\
                 References \\
                 Chapter 3: Network Analysis and Indicators \\
                 3.1 Introduction \\
                 3.2 Networks and Bibliometrics \\
                 3.3 Basic Network Properties \\
                 3.4 Network Data \\
                 3.5 Scientometrics Through Networks \\
                 3.6 Collaboration Networks \\
                 3.7 Citation Networks \\
                 References \\
                 Chapter 4: PageRank-Related Methods for Analyzing
                 Citation Networks \\
                 4.1 Introduction \\
                 4.2 PageRank \\
                 4.3 Literature Review \\
                 4.4 Tutorial \\
                 References \\
                 Part II: The Science System \\
                 Chapter 5: Systems Life Cycle and Its Relation with the
                 Triple Helix \\
                 5.1 Introduction and Motivation \\
                 5.2 Background Work Related to This Study \\
                 5.3 Hypothesis to Test \\
                 5.4 Measurable States During the Life Cycle of a
                 Technology \\
                 5.5 Step-by-Step Use of a Tool to Generate Results \\
                 5.6 Expansion/Evolution of Milestone 5 Concerning
                 Technology Readiness Levels \\
                 5.7 Application of TRL Logic to the Modified Model \\
                 5.8 Discussion \\
                 References \\
                 Chapter 6: Spatial Scientometrics and Scholarly Impact:
                 A Review of Recent Studies, Tools, and Methods \\
                 6.1 Introduction \\
                 6.2 Selection of Reviewed Papers \\
                 6.3 Review \\
                 References \\
                 Chapter 7: Researchers' Publication Patterns and Their
                 Use for Author Disambiguation \\
                 7.1 Introduction \\
                 7.2 Previous Studies on the Attribution of Individual
                 Authors' Publications \\
                 7.3 Methods \\
                 7.4 Regularities in Researchers' Publication Patterns
                 \\
                 Appendix 1: List of Disciplines Assigned to Journals
                 \\
                 Appendix 2: List of Disciplines Assigned to Departments
                 \\
                 References \\
                 Chapter 8: Knowledge Integration and Diffusion:
                 Measures and Mapping of Diversity and Coherence \\
                 8.1 Introduction \\
                 8.2 Conceptual Framework: Knowledge Integration and
                 Diffusion as Shifts in Cognitive Diversity and
                 Coherence \\
                 8.3 Choices on Data and Methods for Operationalisation
                 \\
                 8.4 How to Compute and Visualise Knowledge Integration
                 \\
                 References \\
                 Part III: Statistical and Text-Based Methods \\
                 Chapter 9: Limited Dependent Variable Models and
                 Probabilistic Prediction in Informetrics \\
                 9.1 Introduction \\
                 9.2 The Data: Which Articles Get Cited in Informetrics?
                 \\
                 9.3 Binary Regression \\
                 9.4 Ordinal Regression \\
                 9.5 Count Data Models \\
                 9.6 Limited Dependent Variable Models in Stata \\
                 References \\
                 Chapter 10: Text Mining with the Stanford CoreNLP \\
                 10.1 Introduction \\
                 10.2 Text Mining in Bibliometric Research \\
                 10.3 Text Mining System Architecture \\
                 10.4 The Stanford CoreNLP Parser \\
                 10.5 An Example of Text Mining for Bibliometric
                 Analysis \\
                 10.6 Results \\
                 References \\
                 Chapter 11: Topic Modeling: Measuring Scholarly Impact
                 Using a Topical Lens \\
                 11.1 Introduction \\
                 11.2 Topic Models \\
                 11.3 Applying Topic Modeling Methods in Scholarly
                 Communication \\
                 11.4 Topic Modeling Tool: Case Study \\
                 Appendix: Normalization, Mapping, and Clustering
                 Techniques Used by VOSviewer \\
                 References \\
                 Chapter 12: The Substantive and Practical Significance
                 of Citation Impact Differences Between Institutions:
                 Guidelines for the \ldots{} \\
                 12.1 Introduction \\
                 12.2 Percentile Rankings \\
                 12.3 Data and Statistical Software \\
                 12.4 Effect Sizes and related concepts \\
                 12.5 Cohen's d (for Individual Institutions) \\
                 12.6 Mean Differences Between Institutions \\
                 12.7 Proportions (Both for One Institution and for
                 Comparisons Across Institutions) \\
                 Appendix: Stata Code Used for These Analyses \\
                 References \\
                 Part IV: Visualization \\
                 Chapter 13: Visualizing Bibliometric Networks \\
                 13.1 Introduction \\
                 13.2 Literature Review \\
                 13.3 Software Tools \\
                 13.4 Techniques \\
                 13.5 Tutorials \\
                 Appendix: Normalization, Mapping, and Clustering
                 Techniques Used by VOSviewer \\
                 References \\
                 Chapter 14: Replicable Science of Science Studies \\
                 14.1 Open Tools for Science of Science Studies \\
                 14.2 The Science of Science (Sci2) Tool \\
                 14.3 Career Trajectories \\
                 14.4 Discussion and Outlook \\
                 References \\
                 Index",
}


bibtype: article
filename: pagerank.bib
label: Gleich:2014:MP
author: D. F. Gleich and L.-H. Lim and Y. Yu
title: Multilinear PageRank
journal: arxiv.org
volume: arXiv:1409.1465 [cs.NA]
pages: 1--33
year: 2014
bibdate: Tue Aug 11 16:49:48 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:49:48 ???
URL: http://arxiv.org/pdf/1409.1465v1.pdf
acknowledgement: ack-nhfb
rawauthor: D. F. Gleich and L.-H. Lim and Y. Yu
rawtitle: Multilinear PageRank
entry:
@Article{Gleich:2014:MP,
  author =       "D. F. Gleich and L.-H. Lim and Y. Yu",
  title =        "Multilinear PageRank",
  journal =      "arxiv.org",
  volume =       "arXiv:1409.1465 [cs.NA]",
  pages =        "1--33",
  year =         "2014",
  bibdate =      "Tue Aug 11 16:49:48 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://arxiv.org/pdf/1409.1465v1.pdf",
  acknowledgement = ack-nhfb,
}


bibtype: book
filename: pagerank.bib
label: Leskovec:2014:MMD
author: Jurij Leskovec and Anand Rajaraman and Jeffrey D. Ullman
title: Mining of massive datasets
publisher: pub-CAMBRIDGE
address: pub-CAMBRIDGE:adr
edition: Second
pages: xii + 467
year: 2014
DOI: https://doi.org/10.1017/CBO9781139924801
LCCN: QA76.9.D343 R35 2014eb
bibdate: Wed Jan 7 11:34:18 MST 2015
bibsource: fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.01.07 11:34:18 MST
remark: Previous edition: 2012.
subject: Data mining; Big data
acknowledgement: ack-nhfb
rawauthor: Jurij Leskovec and Anand Rajaraman and Jeffrey D. Ullman
rawtitle: Mining of massive datasets
ISBN:
1-107-07723-0 (hardcover), 1-316-14731-2 (e-book), 1-139-92480-X (e-book) OR 1107077230 (hardcover), 1316147312 (ebook), 113992480X (ebook)

ISBN13:
978-1-107-07723-2 (hardcover), 978-1-316-14731-3 (e-book), 978-1-139-92480-1 (e-book) OR 9781107077232 (hardcover), 9781316147313 (ebook), 9781139924801 (ebook)

abstract:
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

TOC:
Preface 1. Data mining 2. Map-reduce and the new software stack 3. Finding similar items 4. Mining data streams 5. Link analysis 6. Frequent itemsets 7. Clustering 8. Advertising on the Web 9. Recommendation systems 10. Mining social-network graphs 11. Dimensionality reduction 12. Large-scale machine learning Index

rawabstract:
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

rawTOC:
Preface \\ 1. Data mining \\ 2. Map-reduce and the new software stack \\ 3. Finding similar items \\ 4. Mining data streams \\ 5. Link analysis \\ 6. Frequent itemsets \\ 7. Clustering \\ 8. Advertising on the Web \\ 9. Recommendation systems \\ 10. Mining social-network graphs \\ 11. Dimensionality reduction \\ 12. Large-scale machine learning \\ Index

entry:
@Book{Leskovec:2014:MMD,
  author =       "Jurij Leskovec and Anand Rajaraman and Jeffrey D.
                 Ullman",
  title =        "Mining of massive datasets",
  publisher =    pub-CAMBRIDGE,
  address =      pub-CAMBRIDGE:adr,
  edition =      "Second",
  pages =        "xii + 467",
  year =         "2014",
  DOI =          "https://doi.org/10.1017/CBO9781139924801",
  ISBN =         "1-107-07723-0 (hardcover), 1-316-14731-2 (e-book),
                 1-139-92480-X (e-book)",
  ISBN-13 =      "978-1-107-07723-2 (hardcover), 978-1-316-14731-3
                 (e-book), 978-1-139-92480-1 (e-book)",
  LCCN =         "QA76.9.D343 R35 2014eb",
  bibdate =      "Wed Jan 7 11:34:18 MST 2015",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  abstract =     "Written by leading authorities in database and Web
                 technologies, this book is essential reading for
                 students and practitioners alike. The popularity of the
                 Web and Internet commerce provides many extremely large
                 datasets from which information can be gleaned by data
                 mining. This book focuses on practical algorithms that
                 have been used to solve key problems in data mining and
                 can be applied successfully to even the largest
                 datasets. It begins with a discussion of the map-reduce
                 framework, an important tool for parallelizing
                 algorithms automatically. The authors explain the
                 tricks of locality-sensitive hashing and stream
                 processing algorithms for mining data that arrives too
                 fast for exhaustive processing. Other chapters cover
                 the PageRank idea and related tricks for organizing the
                 Web, the problems of finding frequent itemsets and
                 clustering. This second edition includes new and
                 extended coverage on social networks, machine learning
                 and dimensionality reduction.",
  acknowledgement = ack-nhfb,
  remark =       "Previous edition: 2012.",
  subject =      "Data mining; Big data",
  tableofcontents = "Preface \\
                 1. Data mining \\
                 2. Map-reduce and the new software stack \\
                 3. Finding similar items \\
                 4. Mining data streams \\
                 5. Link analysis \\
                 6. Frequent itemsets \\
                 7. Clustering \\
                 8. Advertising on the Web \\
                 9. Recommendation systems \\
                 10. Mining social-network graphs \\
                 11. Dimensionality reduction \\
                 12. Large-scale machine learning \\
                 Index",
}


bibtype: article
filename: pagerank.bib
label: 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
monthnumber: 3
year: 2014
CODEN: IFPLAT
ISSN: 0020-0190 (print), 1872-6119 (electronic) OR 00200190 (print), 18726119 (electronic)
ISSNL: 0020-0190 OR 00200190
bibdate: Mon Dec 9 09:33:47 MST 2013
bibtimestamp: 2013.12.09 09:33:47 MST
URL: http://www.sciencedirect.com/science/article/pii/S0020019013002743
fjournal: Information Processing Letters
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00200190
rawauthor: Peter Lofgren
rawtitle: On the complexity of the {Monte Carlo} method for incremental {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/infoproc2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www.sciencedirect.com/science/journal/00200190

entry:
@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 =    "https://www.math.utah.edu/pub/tex/bib/infoproc2010.bib;
                 https://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",
}


bibtype: article
filename: pagerank.bib
label: Maehara:2014:CPP
author: Takanori Maehara and Takuya Akiba and Yoichi Iwata and Ken-ichi Kawarabayashi
title: Computing personalized PageRank quickly by exploiting graph structures
journal: j-PROC-VLDB-ENDOWMENT
volume: 7
number: 12
pages: 1023--1034
month: aug
monthnumber: 8
year: 2014
ISSN: 2150-8097 OR 21508097
bibdate: Wed Feb 4 17:20:26 MST 2015
bibtimestamp: 2015.02.04 17:20:26 MST
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/citation.cfm?id=J1174
rawauthor: Takanori Maehara and Takuya Akiba and Yoichi Iwata and Ken-ichi Kawarabayashi
rawtitle: Computing personalized {PageRank} quickly by exploiting graph structures
bibsource:
http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbe.bib

abstract:
We propose a new scalable algorithm that can compute Personalized PageRank (PPR) very quickly. The Power method is a state-of-the-art algorithm for computing exact PPR; however, it requires many iterations. Thus reducing the number of iterations is the main challenge. We achieve this by exploiting graph structures of web graphs and social networks. The convergence of our algorithm is very fast. In fact, it requires up to 7.5 times fewer iterations than the Power method and is up to five times faster in actual computation time. To the best of our knowledge, this is the first time to use graph structures explicitly to solve PPR quickly. Our contributions can be summarized as follows. 1. We provide an algorithm for computing a tree decomposition, which is more efficient and scalable than any previous algorithm. 2. Using the above algorithm, we can obtain a core-tree decomposition of any web graph and social network. This allows us to decompose a web graph and a social network into (1) the core, which behaves like an expander graph, and (2) a small tree-width graph, which behaves like a tree in an algorithmic sense. 3. We apply a direct method to the small tree-width graph to construct an LU decomposition. 4. Building on the LU decomposition and using it as pre-conditioner, we apply GMRES method (a state-of-the-art advanced iterative method) to compute PPR for whole web graphs and social networks.

rawabstract:
We propose a new scalable algorithm that can compute Personalized PageRank (PPR) very quickly. The Power method is a state-of-the-art algorithm for computing exact PPR; however, it requires many iterations. Thus reducing the number of iterations is the main challenge. We achieve this by exploiting graph structures of web graphs and social networks. The convergence of our algorithm is very fast. In fact, it requires up to 7.5 times fewer iterations than the Power method and is up to five times faster in actual computation time. To the best of our knowledge, this is the first time to use graph structures explicitly to solve PPR quickly. Our contributions can be summarized as follows. 1. We provide an algorithm for computing a tree decomposition, which is more efficient and scalable than any previous algorithm. 2. Using the above algorithm, we can obtain a core-tree decomposition of any web graph and social network. This allows us to decompose a web graph and a social network into (1) the core, which behaves like an expander graph, and (2) a small tree-width graph, which behaves like a tree in an algorithmic sense. 3. We apply a direct method to the small tree-width graph to construct an LU decomposition. 4. Building on the LU decomposition and using it as pre-conditioner, we apply GMRES method (a state-of-the-art advanced iterative method) to compute PPR for whole web graphs and social networks.

entry:
@Article{Maehara:2014:CPP,
  author =       "Takanori Maehara and Takuya Akiba and Yoichi Iwata and
                 Ken-ichi Kawarabayashi",
  title =        "Computing personalized {PageRank} quickly by
                 exploiting graph structures",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "7",
  number =       "12",
  pages =        "1023--1034",
  month =        aug,
  year =         "2014",
  CODEN =        "????",
  ISSN =         "2150-8097",
  bibdate =      "Wed Feb 4 17:20:26 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  abstract =     "We propose a new scalable algorithm that can compute
                 Personalized PageRank (PPR) very quickly. The Power
                 method is a state-of-the-art algorithm for computing
                 exact PPR; however, it requires many iterations. Thus
                 reducing the number of iterations is the main
                 challenge. We achieve this by exploiting graph
                 structures of web graphs and social networks. The
                 convergence of our algorithm is very fast. In fact, it
                 requires up to 7.5 times fewer iterations than the
                 Power method and is up to five times faster in actual
                 computation time. To the best of our knowledge, this is
                 the first time to use graph structures explicitly to
                 solve PPR quickly. Our contributions can be summarized
                 as follows. 1. We provide an algorithm for computing a
                 tree decomposition, which is more efficient and
                 scalable than any previous algorithm. 2. Using the
                 above algorithm, we can obtain a core-tree
                 decomposition of any web graph and social network. This
                 allows us to decompose a web graph and a social network
                 into (1) the core, which behaves like an expander
                 graph, and (2) a small tree-width graph, which behaves
                 like a tree in an algorithmic sense. 3. We apply a
                 direct method to the small tree-width graph to
                 construct an LU decomposition. 4. Building on the LU
                 decomposition and using it as pre-conditioner, we apply
                 GMRES method (a state-of-the-art advanced iterative
                 method) to compute PPR for whole web graphs and social
                 networks.",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "http://portal.acm.org/citation.cfm?id=J1174",
}


bibtype: article
filename: pagerank.bib
label: Nykl:2014:PVE
author: Michal Nykl and Karel Jezek and Dalibor Fiala and Martin Dostal
title: PageRank variants in the evaluation of citation networks
journal: j-J-INFORMETRICS
volume: 8
number: 3
pages: 683--692
month: jul
monthnumber: 7
year: 2014
ISSN: 1751-1577 (print), 1875-5879 (electronic) OR 17511577 (print), 18755879 (electronic)
ISSNL: 1751-1577 OR 17511577
bibdate: Wed Sep 9 16:29:51 MDT 2015
bibtimestamp: 2015.09.09 16:29:51 MDT
URL: http://www.sciencedirect.com/science/article/pii/S1751157714000583
fjournal: Journal of Informetrics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/17511577/
rawauthor: Michal Nykl and Karel Jezek and Dalibor Fiala and Martin Dostal
rawtitle: {PageRank} variants in the evaluation of citation networks
bibsource:
https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Nykl:2014:PVE,
  author =       "Michal Nykl and Karel Jezek and Dalibor Fiala and
                 Martin Dostal",
  title =        "{PageRank} variants in the evaluation of citation
                 networks",
  journal =      j-J-INFORMETRICS,
  volume =       "8",
  number =       "3",
  pages =        "683--692",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  ISSN =         "1751-1577 (print), 1875-5879 (electronic)",
  ISSN-L =       "1751-1577",
  bibdate =      "Wed Sep 9 16:29:51 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1751157714000583",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Informetrics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/17511577/",
}


bibtype: article
filename: pagerank.bib
label: Salkuyeh:2014:PPG
author: Davod Khojasteh Salkuyeh and Vahid Edalatpour and Davod Hezari
title: Polynomial Preconditioning for the GeneRank Problem
journal: j-ELECTRON-TRANS-NUMER-ANAL
volume: 41
pages: 179--189
year: 2014
ISSN: 1068-9613 (print), 1097-4067 (electronic) OR 10689613 (print), 10974067 (electronic)
ISSNL: 1068-9613 OR 10689613
MRclass: 92D10 (65F10 65F50)
MRnumber: 3232104
bibdate: Mon Apr 3 06:27:15 MDT 2017
bibsource: https://www.math.utah.edu/pub/tex/bib/etna.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2017.04.03 06:27:15 MDT
fjournal: Electronic Transactions on Numerical Analysis
acknowledgement: ack-nhfb
journalURL: http://etna.mcs.kent.edu/
rawauthor: Davod Khojasteh Salkuyeh and Vahid Edalatpour and Davod Hezari
rawtitle: Polynomial Preconditioning for the {GeneRank} Problem
URL:
http://etna.mcs.kent.edu/vol.41.2014/pp179-189.dir/pp179-189.pdf; http://etna.mcs.kent.edu/volumes/2011-2020/vol41/abstract.php?vol=41&pages=179-189

entry:
@Article{Salkuyeh:2014:PPG,
  author =       "Davod Khojasteh Salkuyeh and Vahid Edalatpour and
                 Davod Hezari",
  title =        "Polynomial Preconditioning for the {GeneRank}
                 Problem",
  journal =      j-ELECTRON-TRANS-NUMER-ANAL,
  volume =       "41",
  pages =        "179--189",
  year =         "2014",
  CODEN =        "????",
  ISSN =         "1068-9613 (print), 1097-4067 (electronic)",
  ISSN-L =       "1068-9613",
  MRclass =      "92D10 (65F10 65F50)",
  MRnumber =     "3232104",
  bibdate =      "Mon Apr 3 06:27:15 MDT 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/etna.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://etna.mcs.kent.edu/vol.41.2014/pp179-189.dir/pp179-189.pdf;
                 http://etna.mcs.kent.edu/volumes/2011-2020/vol41/abstract.php?vol=41&pages=179-189",
  acknowledgement = ack-nhfb,
  fjournal =     "Electronic Transactions on Numerical Analysis",
  journal-URL =  "http://etna.mcs.kent.edu/",
}


bibtype: article
filename: pagerank.bib
label: Setayesh:2014:PEV
author: Sara Setayesh and Ali Harounabadi and Amir Masoud Rahmani
journal: j-INT-J-COMP-APPL
volume: 85
pages: 7--10
month: jan
monthnumber: 1
year: 2014
DOI: https://doi.org/10.5120/14931-3357
ISSN: 0975-8887 OR 09758887
ISSNL: 0975-8887 OR 09758887
bibdate: Fri Jan 24 09:03:51 MST 2025
bibtimestamp: 2025.01.24 09:03:51 MST
URL: https://www.ijcaonline.org/archives/volume85/number17/14931-3357/
fjournal: International Journal of Computer Applications
acknowledgement: ack-nhfb
ajournal: Intern. J. of Computer Applications
articleno: 17
journalURL: https://www.ijcaonline.org/
rawauthor: Sara Setayesh and Ali Harounabadi and Amir Masoud Rahmani
title:
Presentation of an Extended Version of the PageRank Algorithm to Rank Web Pages Inspired by Ant Colony Algorithm

bibsource:
https://www.math.utah.edu/pub/tex/bib/intjcompappl.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

rawtitle:
Presentation of an Extended Version of the {PageRank} Algorithm to Rank {Web} Pages Inspired by Ant Colony Algorithm

entry:
@Article{Setayesh:2014:PEV,
  author =       "Sara Setayesh and Ali Harounabadi and Amir Masoud
                 Rahmani",
  title =        "Presentation of an Extended Version of the {PageRank}
                 Algorithm to Rank {Web} Pages Inspired by Ant Colony
                 Algorithm",
  journal =      j-INT-J-COMP-APPL,
  volume =       "85",
  number =       "??",
  pages =        "7--10",
  month =        jan,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.5120/14931-3357",
  ISSN =         "0975-8887",
  ISSN-L =       "0975-8887",
  bibdate =      "Fri Jan 24 09:03:51 MST 2025",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/intjcompappl.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://www.ijcaonline.org/archives/volume85/number17/14931-3357/",
  acknowledgement = ack-nhfb,
  ajournal =     "Intern. J. of Computer Applications",
  articleno =    "17",
  fjournal =     "International Journal of Computer Applications",
  journal-URL =  "https://www.ijcaonline.org/",
}


bibtype: article
filename: pagerank.bib
label: Wang:2014:GRC
author: Qing Wang and Siyi Zhang and Shichao Pang and Menghuan Zhang and Bo Wang and Qi Liu and Jing Li
title: GroupRank: Rank Candidate Genes in PPI Network by Differentially Expressed Gene Groups
journal: j-PLOS-ONE
volume: 9
number: 10
pages: e110406:1--e110406:7
month: oct
monthnumber: 10
year: 2014
CODEN: POLNCL
DOI: https://doi.org/10.1371/journal.pone.0110406
ISSN: 1932-6203 OR 19326203
bibdate: Wed Aug 12 08:52:01 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.12 08:52:01 ???
URL: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0110406
fjournal: PLoS One
acknowledgement: ack-nhfb
journalURL: http://www.plosone.org/
rawauthor: Qing Wang and Siyi Zhang and Shichao Pang and Menghuan Zhang and Bo Wang and Qi Liu and Jing Li
rawtitle: {GroupRank}: Rank Candidate Genes in {PPI} Network by Differentially Expressed Gene Groups
abstract:
Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated. In this study, based on an assumption that a strong candidate disease gene is more likely close to gene groups in which all members coordinately differentially express than individual genes with differential expression, we developed a novel disease gene prioritization method GroupRank by integrating gene co-expression and differential expression information generated from microarray data as well as PPI network. A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network. We tested our method on data sets of lung, kidney, leukemia and breast cancer. The results revealed GroupRank could efficiently prioritize disease genes with significantly improved AUC value in comparison to the previous method with no consideration of co-expressed gene groups in PPI network. Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.

rawabstract:
Many cell activities are organized as a network, and genes are clustered into co-expressed groups if they have the same or closely related biological function or they are co-regulated. In this study, based on an assumption that a strong candidate disease gene is more likely close to gene groups in which all members coordinately differentially express than individual genes with differential expression, we developed a novel disease gene prioritization method GroupRank by integrating gene co-expression and differential expression information generated from microarray data as well as PPI network. A candidate gene is ranked high using GroupRank if it is differentially expressed in disease and control or is close to differentially co-expressed groups in PPI network. We tested our method on data sets of lung, kidney, leukemia and breast cancer. The results revealed GroupRank could efficiently prioritize disease genes with significantly improved AUC value in comparison to the previous method with no consideration of co-expressed gene groups in PPI network. Moreover, the functional analyses of the major contributing gene group in gene prioritization of kidney cancer verified that our algorithm GroupRank not only ranks disease genes efficiently but also could help us identify and understand possible mechanisms in important physiological and pathological processes of disease.

entry:
@Article{Wang:2014:GRC,
  author =       "Qing Wang and Siyi Zhang and Shichao Pang and Menghuan
                 Zhang and Bo Wang and Qi Liu and Jing Li",
  title =        "{GroupRank}: Rank Candidate Genes in {PPI} Network by
                 Differentially Expressed Gene Groups",
  journal =      j-PLOS-ONE,
  volume =       "9",
  number =       "10",
  pages =        "e110406:1--e110406:7",
  month =        oct,
  year =         "2014",
  CODEN =        "POLNCL",
  DOI =          "https://doi.org/10.1371/journal.pone.0110406",
  ISSN =         "1932-6203",
  bibdate =      "Wed Aug 12 08:52:01 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0110406",
  abstract =     "Many cell activities are organized as a network, and
                 genes are clustered into co-expressed groups if they
                 have the same or closely related biological function or
                 they are co-regulated. In this study, based on an
                 assumption that a strong candidate disease gene is more
                 likely close to gene groups in which all members
                 coordinately differentially express than individual
                 genes with differential expression, we developed a
                 novel disease gene prioritization method GroupRank by
                 integrating gene co-expression and differential
                 expression information generated from microarray data
                 as well as PPI network. A candidate gene is ranked high
                 using GroupRank if it is differentially expressed in
                 disease and control or is close to differentially
                 co-expressed groups in PPI network. We tested our
                 method on data sets of lung, kidney, leukemia and
                 breast cancer. The results revealed GroupRank could
                 efficiently prioritize disease genes with significantly
                 improved AUC value in comparison to the previous method
                 with no consideration of co-expressed gene groups in
                 PPI network. Moreover, the functional analyses of the
                 major contributing gene group in gene prioritization of
                 kidney cancer verified that our algorithm GroupRank not
                 only ranks disease genes efficiently but also could
                 help us identify and understand possible mechanisms in
                 important physiological and pathological processes of
                 disease.",
  acknowledgement = ack-nhfb,
  fjournal =     "PLoS One",
  journal-URL =  "http://www.plosone.org/",
}


bibtype: article
filename: pagerank.bib
label: Yan:2014:TBP
author: Erjia Yan
title: Topic-based PageRank: toward a topic-level scientific evaluation
journal: j-SCIENTOMETRICS
volume: 100
number: 2
pages: 407--437
month: aug
monthnumber: 8
year: 2014
CODEN: SCNTDX
DOI: https://doi.org/10.1007/s11192-014-1308-5
ISSN: 0138-9130 (print), 1588-2861 (electronic) OR 01389130 (print), 15882861 (electronic)
ISSNL: 0138-9130 OR 01389130
bibdate: Wed Sep 2 12:06:03 MDT 2015
bibtimestamp: 2015.09.02 12:06:03 MDT
URL: http://link.springer.com/article/10.1007/s11192-014-1308-5
fjournal: Scientometrics
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11192
rawauthor: Erjia Yan
rawtitle: Topic-based {PageRank}: toward a topic-level scientific evaluation
bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib

entry:
@Article{Yan:2014:TBP,
  author =       "Erjia Yan",
  title =        "Topic-based {PageRank}: toward a topic-level
                 scientific evaluation",
  journal =      j-SCIENTOMETRICS,
  volume =       "100",
  number =       "2",
  pages =        "407--437",
  month =        aug,
  year =         "2014",
  CODEN =        "SCNTDX",
  DOI =          "https://doi.org/10.1007/s11192-014-1308-5",
  ISSN =         "0138-9130 (print), 1588-2861 (electronic)",
  ISSN-L =       "0138-9130",
  bibdate =      "Wed Sep 2 12:06:03 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib",
  URL =          "http://link.springer.com/article/10.1007/s11192-014-1308-5",
  acknowledgement = ack-nhfb,
  fjournal =     "Scientometrics",
  journal-URL =  "http://link.springer.com/journal/11192",
}


bibtype: article
filename: pagerank.bib
label: Chen:2015:AAS
author: Hung-Hsuan Chen and C. Lee Giles
title: ASCOS++: an Asymmetric Similarity Measure for Weighted Networks to Address the Problem of SimRank
journal: j-TKDD
volume: 10
number: 2
pages: 15:1--15:??
month: oct
monthnumber: 10
year: 2015
DOI: https://doi.org/10.1145/2776894
ISSN: 1556-4681 (print), 1556-472X (electronic) OR 15564681 (print), 1556472X (electronic)
ISSNL: 1556-4681 OR 15564681
bibdate: Mon Oct 26 17:19:18 MDT 2015
bibtimestamp: 2015.10.26 17:19:18 MDT
fjournal: ACM Transactions on Knowledge Discovery from Data (TKDD)
acknowledgement: ack-nhfb
articleno: 15
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J1054
rawauthor: Hung-Hsuan Chen and C. Lee Giles
bibsource:
http://www.acm.org/pubs/contents/journals/tkdd/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tkdd.bib

abstract:
In this article, we explore the relationships among digital objects in terms of their similarity based on vertex similarity measures. We argue that SimRank --- a famous similarity measure --- and its families, such as P-Rank and SimRank++, fail to capture similar node pairs in certain conditions, especially when two nodes can only reach each other through paths of odd lengths. We present new similarity measures ASCOS and ASCOS++ to address the problem. ASCOS outputs a more complete similarity score than SimRank and SimRank's families. ASCOS++ enriches ASCOS to include edge weight into the measure, giving all edges and network weights an opportunity to make their contribution. We show that both ASCOS++ and ASCOS can be reformulated and applied on a distributed environment for parallel contribution. Experimental results show that ASCOS++ reports a better score than SimRank and several famous similarity measures. Finally, we re-examine previous use cases of SimRank, and explain appropriate and inappropriate use cases. We suggest future SimRank users following the rules proposed here before naively applying it. We also discuss the relationship between ASCOS++ and PageRank.

rawabstract:
In this article, we explore the relationships among digital objects in terms of their similarity based on vertex similarity measures. We argue that SimRank --- a famous similarity measure --- and its families, such as P-Rank and SimRank++, fail to capture similar node pairs in certain conditions, especially when two nodes can only reach each other through paths of odd lengths. We present new similarity measures ASCOS and ASCOS++ to address the problem. ASCOS outputs a more complete similarity score than SimRank and SimRank's families. ASCOS++ enriches ASCOS to include edge weight into the measure, giving all edges and network weights an opportunity to make their contribution. We show that both ASCOS++ and ASCOS can be reformulated and applied on a distributed environment for parallel contribution. Experimental results show that ASCOS++ reports a better score than SimRank and several famous similarity measures. Finally, we re-examine previous use cases of SimRank, and explain appropriate and inappropriate use cases. We suggest future SimRank users following the rules proposed here before na{\"\i}vely applying it. We also discuss the relationship between ASCOS++ and PageRank.

rawtitle:
{ASCOS++}: an Asymmetric Similarity Measure for Weighted Networks to Address the Problem of {SimRank}

entry:
@Article{Chen:2015:AAS,
  author =       "Hung-Hsuan Chen and C. Lee Giles",
  title =        "{ASCOS++}: an Asymmetric Similarity Measure for
                 Weighted Networks to Address the Problem of {SimRank}",
  journal =      j-TKDD,
  volume =       "10",
  number =       "2",
  pages =        "15:1--15:??",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2776894",
  ISSN =         "1556-4681 (print), 1556-472X (electronic)",
  ISSN-L =       "1556-4681",
  bibdate =      "Mon Oct 26 17:19:18 MDT 2015",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tkdd/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tkdd.bib",
  abstract =     "In this article, we explore the relationships among
                 digital objects in terms of their similarity based on
                 vertex similarity measures. We argue that SimRank --- a
                 famous similarity measure --- and its families, such as
                 P-Rank and SimRank++, fail to capture similar node
                 pairs in certain conditions, especially when two nodes
                 can only reach each other through paths of odd lengths.
                 We present new similarity measures ASCOS and ASCOS++ to
                 address the problem. ASCOS outputs a more complete
                 similarity score than SimRank and SimRank's families.
                 ASCOS++ enriches ASCOS to include edge weight into the
                 measure, giving all edges and network weights an
                 opportunity to make their contribution. We show that
                 both ASCOS++ and ASCOS can be reformulated and applied
                 on a distributed environment for parallel contribution.
                 Experimental results show that ASCOS++ reports a better
                 score than SimRank and several famous similarity
                 measures. Finally, we re-examine previous use cases of
                 SimRank, and explain appropriate and inappropriate use
                 cases. We suggest future SimRank users following the
                 rules proposed here before na{\"\i}vely applying it. We
                 also discuss the relationship between ASCOS++ and
                 PageRank.",
  acknowledgement = ack-nhfb,
  articleno =    "15",
  fjournal =     "ACM Transactions on Knowledge Discovery from Data
                 (TKDD)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1054",
}


bibtype: article
filename: pagerank.bib
label: Dong:2015:APP
author: Wenqiang Dong and Fulai Wang and Yu Huang and Guangluan Xu and Zhi Guo and Xingyu Fu and Kun Fu
journal: j-COMPUTERS-AND-GRAPHICS
volume: 47
pages: 24--33
month: apr
monthnumber: 4
year: 2015
CODEN: COGRD2
ISSN: 0097-8493 (print), 1873-7684 (electronic) OR 00978493 (print), 18737684 (electronic)
ISSNL: 0097-8493 OR 00978493
bibdate: Sat Mar 14 08:21:38 MDT 2015
bibtimestamp: 2015.03.14 08:21:38 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0097849314001277
fjournal: Computers & Graphics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00978493/
rawauthor: Wenqiang Dong and Fulai Wang and Yu Huang and Guangluan Xu and Zhi Guo and Xingyu Fu and Kun Fu
title:
An advanced pre-positioning method for the force-directed graph visualization based on PageRank algorithm

bibsource:
https://www.math.utah.edu/pub/tex/bib/compgraph.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

rawtitle:
An advanced pre-positioning method for the force-directed graph visualization based on {PageRank} algorithm

entry:
@Article{Dong:2015:APP,
  author =       "Wenqiang Dong and Fulai Wang and Yu Huang and
                 Guangluan Xu and Zhi Guo and Xingyu Fu and Kun Fu",
  title =        "An advanced pre-positioning method for the
                 force-directed graph visualization based on {PageRank}
                 algorithm",
  journal =      j-COMPUTERS-AND-GRAPHICS,
  volume =       "47",
  number =       "??",
  pages =        "24--33",
  month =        apr,
  year =         "2015",
  CODEN =        "COGRD2",
  ISSN =         "0097-8493 (print), 1873-7684 (electronic)",
  ISSN-L =       "0097-8493",
  bibdate =      "Sat Mar 14 08:21:38 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/compgraph.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0097849314001277",
  acknowledgement = ack-nhfb,
  fjournal =     "Computers \& Graphics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00978493/",
}


bibtype: article
filename: pagerank.bib
label: Fiala:2015:DPB
author: Dalibor Fiala and Lovro Subelj and Slavko Zitnik and Marko Bajec
title: Do PageRank-based author rankings outperform simple citation counts?
journal: j-J-INFORMETRICS
volume: 9
number: 2
pages: 334--348
month: apr
monthnumber: 4
year: 2015
ISSN: 1751-1577 (print), 1875-5879 (electronic) OR 17511577 (print), 18755879 (electronic)
ISSNL: 1751-1577 OR 17511577
bibdate: Wed Sep 9 16:29:52 MDT 2015
bibtimestamp: 2015.09.09 16:29:52 MDT
URL: http://www.sciencedirect.com/science/article/pii/S1751157715000267
fjournal: Journal of Informetrics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/17511577/
rawauthor: Dalibor Fiala and Lovro Subelj and Slavko Zitnik and Marko Bajec
rawtitle: Do {PageRank}-based author rankings outperform simple citation counts?
bibsource:
https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Fiala:2015:DPB,
  author =       "Dalibor Fiala and Lovro Subelj and Slavko Zitnik and
                 Marko Bajec",
  title =        "Do {PageRank}-based author rankings outperform simple
                 citation counts?",
  journal =      j-J-INFORMETRICS,
  volume =       "9",
  number =       "2",
  pages =        "334--348",
  month =        apr,
  year =         "2015",
  CODEN =        "????",
  ISSN =         "1751-1577 (print), 1875-5879 (electronic)",
  ISSN-L =       "1751-1577",
  bibdate =      "Wed Sep 9 16:29:52 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1751157715000267",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Informetrics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/17511577/",
}


bibtype: article
filename: pagerank.bib
label: Gleich:2015:MP
author: David F. Gleich and Lek-Heng Lim and Yongyang Yu
title: Multilinear PageRank
journal: j-SIAM-J-MAT-ANA-APPL
volume: 36
number: 4
pages: 1507--1541
year: 2015
CODEN: SJMAEL
DOI: https://doi.org/10.1137/140985160
ISSN: 0895-4798 (print), 1095-7162 (electronic) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
bibdate: Tue Feb 9 08:35:01 MST 2016
bibtimestamp: 2016.02.09 08:35:01 MST
fjournal: SIAM Journal on Matrix Analysis and Applications
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
onlinedate: January 2015
rawauthor: David F. Gleich and Lek-Heng Lim and Yongyang Yu
rawtitle: Multilinear {PageRank}
bibsource:
http://epubs.siam.org/sam-bin/dbq/toc/SIMAX/36/4; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/siamjmatanaappl.bib

entry:
@Article{Gleich:2015:MP,
  author =       "David F. Gleich and Lek-Heng Lim and Yongyang Yu",
  title =        "Multilinear {PageRank}",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "36",
  number =       "4",
  pages =        "1507--1541",
  month =        "????",
  year =         "2015",
  CODEN =        "SJMAEL",
  DOI =          "https://doi.org/10.1137/140985160",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  bibdate =      "Tue Feb 9 08:35:01 MST 2016",
  bibsource =    "http://epubs.siam.org/sam-bin/dbq/toc/SIMAX/36/4;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/siamjmatanaappl.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
  onlinedate =   "January 2015",
}


bibtype: article
filename: pagerank.bib
label: Gleich:2015:PBW
author: David F. Gleich
title: PageRank Beyond the Web
journal: j-SIAM-REVIEW
volume: 57
number: 3
pages: 321--363
year: 2015
CODEN: SIREAD
DOI: https://doi.org/10.1137/140976649
ISSN: 0036-1445 (print), 1095-7200 (electronic) OR 00361445 (print), 10957200 (electronic)
ISSNL: 0036-1445 OR 00361445
bibdate: Sat Aug 8 06:17:25 MDT 2015
bibtimestamp: 2015.08.08 06:17:25 MDT
fjournal: SIAM Review
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sirev
onlinedate: January 2015
rawauthor: David F. Gleich
rawtitle: {PageRank} Beyond the {Web}
bibsource:
http://epubs.siam.org/toc/siread/57/3; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/siamreview.bib

keywords:
AuthorRank; BadRank; BookRank; BuddyRank; CiteRank; DirRank; FactRank; FolkRank; GeneRank; HostRank; IsoRank; ItemRank; MonitorRank; ObjectRank; PageRank; PopRank; ProteinRank; TimedPageRank; TrustRank; TwitterRank; VisualRank

entry:
@Article{Gleich:2015:PBW,
  author =       "David F. Gleich",
  title =        "{PageRank} Beyond the {Web}",
  journal =      j-SIAM-REVIEW,
  volume =       "57",
  number =       "3",
  pages =        "321--363",
  month =        "????",
  year =         "2015",
  CODEN =        "SIREAD",
  DOI =          "https://doi.org/10.1137/140976649",
  ISSN =         "0036-1445 (print), 1095-7200 (electronic)",
  ISSN-L =       "0036-1445",
  bibdate =      "Sat Aug 8 06:17:25 MDT 2015",
  bibsource =    "http://epubs.siam.org/toc/siread/57/3;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/siamreview.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Review",
  journal-URL =  "http://epubs.siam.org/sirev",
  keywords =     "AuthorRank; BadRank; BookRank; BuddyRank; CiteRank;
                 DirRank; FactRank; FolkRank; GeneRank; HostRank;
                 IsoRank; ItemRank; MonitorRank; ObjectRank; PageRank;
                 PopRank; ProteinRank; TimedPageRank; TrustRank;
                 TwitterRank; VisualRank",
  onlinedate =   "January 2015",
}


bibtype: article
filename: pagerank.bib
label: Grolmusz:2015:NPU
author: Vince Grolmusz
title: A note on the PageRank of undirected graphs
journal: j-INFO-PROC-LETT
volume: 115
number: 6--8
pages: 633--634
month: jun
monthnumber: 6
year: 2015
CODEN: IFPLAT
ISSN: 0020-0190 (print), 1872-6119 (electronic) OR 00200190 (print), 18726119 (electronic)
ISSNL: 0020-0190 OR 00200190
bibdate: Thu May 28 06:03:49 MDT 2015
bibtimestamp: 2015.05.28 06:03:49 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0020019015000381
fjournal: Information Processing Letters
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00200190/
rawauthor: Vince Grolmusz
rawtitle: A note on the {PageRank} of undirected graphs
bibsource:
https://www.math.utah.edu/pub/tex/bib/infoproc2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Grolmusz:2015:NPU,
  author =       "Vince Grolmusz",
  title =        "A note on the {PageRank} of undirected graphs",
  journal =      j-INFO-PROC-LETT,
  volume =       "115",
  number =       "6--8",
  pages =        "633--634",
  month =        jun # "\slash " # aug,
  year =         "2015",
  CODEN =        "IFPLAT",
  ISSN =         "0020-0190 (print), 1872-6119 (electronic)",
  ISSN-L =       "0020-0190",
  bibdate =      "Thu May 28 06:03:49 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/infoproc2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0020019015000381",
  acknowledgement = ack-nhfb,
  fjournal =     "Information Processing Letters",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00200190/",
}


bibtype: article
filename: pagerank.bib
label: Gu:2015:TSM
author: Chuanqing Gu and Fei Xie and Ke Zhang
title: A two-step matrix splitting iteration for computing PageRank
journal: j-J-COMPUT-APPL-MATH
volume: 278
pages: 19--28
day: 15
month: apr
monthnumber: 4
year: 2015
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Sat Feb 25 13:34:48 MST 2017
bibtimestamp: 2017.02.25 13:34:48 MST
URL: http://www.sciencedirect.com/science/article/pii/S0377042714004294
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Chuanqing Gu and Fei Xie and Ke Zhang
rawtitle: A two-step matrix splitting iteration for computing {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Gu:2015:TSM,
  author =       "Chuanqing Gu and Fei Xie and Ke Zhang",
  title =        "A two-step matrix splitting iteration for computing
                 {PageRank}",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "278",
  number =       "??",
  pages =        "19--28",
  day =          "15",
  month =        apr,
  year =         "2015",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Sat Feb 25 13:34:48 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042714004294",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
}


bibtype: article
filename: pagerank.bib
label: Huang:2015:PMI
author: Na Huang and Chang-Feng Ma
title: Parallel multisplitting iteration methods based on $M$-splitting for the PageRank problem
journal: j-APPL-MATH-COMP
volume: 271
pages: 337--343
day: 15
month: nov
monthnumber: 11
year: 2015
CODEN: AMHCBQ
ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
bibdate: Fri Nov 13 08:52:33 MST 2015
bibtimestamp: 2015.11.13 08:52:33 MST
URL: http://www.sciencedirect.com/science/article/pii/S0096300315012345
fjournal: Applied Mathematics and Computation
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00963003/
rawauthor: Na Huang and Chang-Feng Ma
rawtitle: Parallel multisplitting iteration methods based on {$M$}-splitting for the {PageRank} problem
bibsource:
https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Huang:2015:PMI,
  author =       "Na Huang and Chang-Feng Ma",
  title =        "Parallel multisplitting iteration methods based on
                 {$M$}-splitting for the {PageRank} problem",
  journal =      j-APPL-MATH-COMP,
  volume =       "271",
  number =       "??",
  pages =        "337--343",
  day =          "15",
  month =        nov,
  year =         "2015",
  CODEN =        "AMHCBQ",
  ISSN =         "0096-3003 (print), 1873-5649 (electronic)",
  ISSN-L =       "0096-3003",
  bibdate =      "Fri Nov 13 08:52:33 MST 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0096300315012345",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Mathematics and Computation",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00963003/",
}


bibtype: article
filename: pagerank.bib
label: Li:2015:WCP
author: Zhenguo Li and Yixiang Fang and Qin Liu and Jiefeng Cheng and Reynold Cheng and John C. S. Lui
title: Walking in the cloud: parallel SimRank at scale
journal: j-PROC-VLDB-ENDOWMENT
volume: 9
number: 1
pages: 24--35
month: sep
monthnumber: 9
year: 2015
ISSN: 2150-8097 OR 21508097
bibdate: Sat Dec 19 17:42:24 MST 2015
bibtimestamp: 2015.12.19 17:42:24 MST
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/citation.cfm?id=J1174
rawauthor: Zhenguo Li and Yixiang Fang and Qin Liu and Jiefeng Cheng and Reynold Cheng and John C. S. Lui
rawtitle: Walking in the cloud: parallel {SimRank} at scale
abstract:
Despite its popularity, SimRank is computationally costly, in both time and space. In particular, its recursive nature poses a great challenge in using modern distributed computing power, and also prevents querying similarities individually. Existing solutions suffer greatly from these practical issues. In this paper, we break such dependency for maximum efficiency possible. Our method consists of offline and online phases. In offline phase, a length- n indexing vector is derived by solving a linear system in parallel. At online query time, the similarities are computed instantly from the index vector. Throughout, the Monte Carlo method is used to maximally reduce time and space. Our algorithm, called CloudWalker, is highly parallelizable, with only linear time and space. Remarkably, it responses to both single-pair and single-source queries in constant time. CloudWalker is orders of magnitude more efficient and scalable than existing solutions for large-scale problems. Implemented on Spark with 10 machines and tested on the web-scale clue-web graph with 1 billion nodes and 43 billion edges, it takes 110 hours for offline indexing, 64 seconds for a single-pair query, and 188 seconds for a single-source query. To the best of our knowledge, our work is the first to report results on clue-web, which is 10x larger than the largest graph ever reported for SimRank computation.

rawabstract:
Despite its popularity, SimRank is computationally costly, in both time and space. In particular, its recursive nature poses a great challenge in using modern distributed computing power, and also prevents querying similarities individually. Existing solutions suffer greatly from these practical issues. In this paper, we break such dependency for maximum efficiency possible. Our method consists of offline and online phases. In offline phase, a length- n indexing vector is derived by solving a linear system in parallel. At online query time, the similarities are computed instantly from the index vector. Throughout, the Monte Carlo method is used to maximally reduce time and space. Our algorithm, called CloudWalker, is highly parallelizable, with only linear time and space. Remarkably, it responses to both single-pair and single-source queries in constant time. CloudWalker is orders of magnitude more efficient and scalable than existing solutions for large-scale problems. Implemented on Spark with 10 machines and tested on the web-scale clue-web graph with 1 billion nodes and 43 billion edges, it takes 110 hours for offline indexing, 64 seconds for a single-pair query, and 188 seconds for a single-source query. To the best of our knowledge, our work is the first to report results on clue-web, which is 10x larger than the largest graph ever reported for SimRank computation.

entry:
@Article{Li:2015:WCP,
  author =       "Zhenguo Li and Yixiang Fang and Qin Liu and Jiefeng
                 Cheng and Reynold Cheng and John C. S. Lui",
  title =        "Walking in the cloud: parallel {SimRank} at scale",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "9",
  number =       "1",
  pages =        "24--35",
  month =        sep,
  year =         "2015",
  CODEN =        "????",
  ISSN =         "2150-8097",
  bibdate =      "Sat Dec 19 17:42:24 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  abstract =     "Despite its popularity, SimRank is computationally
                 costly, in both time and space. In particular, its
                 recursive nature poses a great challenge in using
                 modern distributed computing power, and also prevents
                 querying similarities individually. Existing solutions
                 suffer greatly from these practical issues. In this
                 paper, we break such dependency for maximum efficiency
                 possible. Our method consists of offline and online
                 phases. In offline phase, a length- n indexing vector
                 is derived by solving a linear system in parallel. At
                 online query time, the similarities are computed
                 instantly from the index vector. Throughout, the Monte
                 Carlo method is used to maximally reduce time and
                 space. Our algorithm, called CloudWalker, is highly
                 parallelizable, with only linear time and space.
                 Remarkably, it responses to both single-pair and
                 single-source queries in constant time. CloudWalker is
                 orders of magnitude more efficient and scalable than
                 existing solutions for large-scale problems.
                 Implemented on Spark with 10 machines and tested on the
                 web-scale clue-web graph with 1 billion nodes and 43
                 billion edges, it takes 110 hours for offline indexing,
                 64 seconds for a single-pair query, and 188 seconds for
                 a single-source query. To the best of our knowledge,
                 our work is the first to report results on clue-web,
                 which is 10x larger than the largest graph ever
                 reported for SimRank computation.",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "http://portal.acm.org/citation.cfm?id=J1174",
}


bibtype: article
filename: pagerank.bib
label: Liu:2015:PCU
author: Zifan Liu and Nahid Emad and Soufian Ben Amor
journal: j-INT-J-PARALLEL-PROG
volume: 43
number: 6
pages: 1028--1053
month: dec
monthnumber: 12
year: 2015
CODEN: IJPPE5
DOI: https://doi.org/10.1007/s10766-014-0344-3
ISSN: 0885-7458 (print), 1573-7640 (electronic) OR 08857458 (print), 15737640 (electronic)
ISSNL: 0885-7458 OR 08857458
bibdate: Tue Sep 29 10:13:48 MDT 2015
bibtimestamp: 2015.09.29 10:13:48 MDT
URL: http://link.springer.com/article/10.1007/s10766-014-0344-3
fjournal: International Journal of Parallel Programming
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/10766
rawauthor: Zifan Liu and Nahid Emad and Soufian Ben Amor
title:
PageRank Computation Using a Multiple Implicitly Restarted Arnoldi Method for Modeling Epidemic Spread

bibsource:
http://link.springer.com/journal/10766/43/6; https://www.math.utah.edu/pub/tex/bib/intjparallelprogram.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

rawtitle:
{PageRank} Computation Using a Multiple Implicitly Restarted {Arnoldi} Method for Modeling Epidemic Spread

entry:
@Article{Liu:2015:PCU,
  author =       "Zifan Liu and Nahid Emad and Soufian Ben Amor",
  title =        "{PageRank} Computation Using a Multiple Implicitly
                 Restarted {Arnoldi} Method for Modeling Epidemic
                 Spread",
  journal =      j-INT-J-PARALLEL-PROG,
  volume =       "43",
  number =       "6",
  pages =        "1028--1053",
  month =        dec,
  year =         "2015",
  CODEN =        "IJPPE5",
  DOI =          "https://doi.org/10.1007/s10766-014-0344-3",
  ISSN =         "0885-7458 (print), 1573-7640 (electronic)",
  ISSN-L =       "0885-7458",
  bibdate =      "Tue Sep 29 10:13:48 MDT 2015",
  bibsource =    "http://link.springer.com/journal/10766/43/6;
                 https://www.math.utah.edu/pub/tex/bib/intjparallelprogram.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/article/10.1007/s10766-014-0344-3",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Parallel Programming",
  journal-URL =  "http://link.springer.com/journal/10766",
}


bibtype: article
filename: pagerank.bib
label: Mitliagkas:2015:FFP
author: Ioannis Mitliagkas and Michael Borokhovich and Alexandros G. Dimakis and Constantine Caramanis
title: FrogWild!: fast PageRank approximations on graph engines
journal: j-PROC-VLDB-ENDOWMENT
volume: 8
number: 8
pages: 874--885
month: apr
monthnumber: 4
year: 2015
ISSN: 2150-8097 OR 21508097
bibdate: Wed Apr 15 19:02:29 MDT 2015
bibtimestamp: 2015.04.15 19:02:29 MDT
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/citation.cfm?id=J1174
rawauthor: Ioannis Mitliagkas and Michael Borokhovich and Alexandros G. Dimakis and Constantine Caramanis
rawtitle: {FrogWild!}: fast {PageRank} approximations on graph engines
abstract:
We propose FrogWild, a novel algorithm for fast approximation of high PageRank vertices, geared towards reducing network costs of running traditional PageRank algorithms. Our algorithm can be seen as a quantized version of power iteration that performs multiple parallel random walks over a directed graph. One important innovation is that we introduce a modification to the GraphLab framework that only partially synchronizes mirror vertices. This partial synchronization vastly reduces the network traffic generated by traditional PageRank algorithms, thus greatly reducing the per-iteration cost of PageRank. On the other hand, this partial synchronization also creates dependencies between the random walks used to estimate PageRank. Our main theoretical innovation is the analysis of the correlations introduced by this partial synchronization process and a bound establishing that our approximation is close to the true PageRank vector. We implement our algorithm in GraphLab and compare it against the default PageRank implementation. We show that our algorithm is very fast, performing each iteration in less than one second on the Twitter graph and can be up to $ 7 times $ faster compared to the standard GraphLab PageRank implementation.

rawabstract:
We propose FrogWild, a novel algorithm for fast approximation of high PageRank vertices, geared towards reducing network costs of running traditional PageRank algorithms. Our algorithm can be seen as a quantized version of power iteration that performs multiple parallel random walks over a directed graph. One important innovation is that we introduce a modification to the GraphLab framework that only partially synchronizes mirror vertices. This partial synchronization vastly reduces the network traffic generated by traditional PageRank algorithms, thus greatly reducing the per-iteration cost of PageRank. On the other hand, this partial synchronization also creates dependencies between the random walks used to estimate PageRank. Our main theoretical innovation is the analysis of the correlations introduced by this partial synchronization process and a bound establishing that our approximation is close to the true PageRank vector. We implement our algorithm in GraphLab and compare it against the default PageRank implementation. We show that our algorithm is very fast, performing each iteration in less than one second on the Twitter graph and can be up to $ 7 \times $ faster compared to the standard GraphLab PageRank implementation.

entry:
@Article{Mitliagkas:2015:FFP,
  author =       "Ioannis Mitliagkas and Michael Borokhovich and
                 Alexandros G. Dimakis and Constantine Caramanis",
  title =        "{FrogWild!}: fast {PageRank} approximations on graph
                 engines",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "8",
  number =       "8",
  pages =        "874--885",
  month =        apr,
  year =         "2015",
  CODEN =        "????",
  ISSN =         "2150-8097",
  bibdate =      "Wed Apr 15 19:02:29 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  abstract =     "We propose FrogWild, a novel algorithm for fast
                 approximation of high PageRank vertices, geared towards
                 reducing network costs of running traditional PageRank
                 algorithms. Our algorithm can be seen as a quantized
                 version of power iteration that performs multiple
                 parallel random walks over a directed graph. One
                 important innovation is that we introduce a
                 modification to the GraphLab framework that only
                 partially synchronizes mirror vertices. This partial
                 synchronization vastly reduces the network traffic
                 generated by traditional PageRank algorithms, thus
                 greatly reducing the per-iteration cost of PageRank. On
                 the other hand, this partial synchronization also
                 creates dependencies between the random walks used to
                 estimate PageRank. Our main theoretical innovation is
                 the analysis of the correlations introduced by this
                 partial synchronization process and a bound
                 establishing that our approximation is close to the
                 true PageRank vector. We implement our algorithm in
                 GraphLab and compare it against the default PageRank
                 implementation. We show that our algorithm is very
                 fast, performing each iteration in less than one second
                 on the Twitter graph and can be up to $ 7 \times $
                 faster compared to the standard GraphLab PageRank
                 implementation.",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "http://portal.acm.org/citation.cfm?id=J1174",
}


bibtype: article
filename: pagerank.bib
label: Nykl:2015:ARB
author: Michal Nykl and Michal Campr and Karel Jezek
title: Author ranking based on personalized PageRank
journal: j-J-INFORMETRICS
volume: 9
number: 4
pages: 777--799
month: oct
monthnumber: 10
year: 2015
ISSN: 1751-1577 (print), 1875-5879 (electronic) OR 17511577 (print), 18755879 (electronic)
ISSNL: 1751-1577 OR 17511577
bibdate: Wed Sep 9 16:29:53 MDT 2015
bibtimestamp: 2015.09.09 16:29:53 MDT
URL: http://www.sciencedirect.com/science/article/pii/S1751157715200181
fjournal: Journal of Informetrics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/17511577/
rawauthor: Michal Nykl and Michal Campr and Karel Jezek
rawtitle: Author ranking based on personalized {PageRank}
entry:
@Article{Nykl:2015:ARB,
  author =       "Michal Nykl and Michal Campr and Karel Jezek",
  title =        "Author ranking based on personalized {PageRank}",
  journal =      j-J-INFORMETRICS,
  volume =       "9",
  number =       "4",
  pages =        "777--799",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  ISSN =         "1751-1577 (print), 1875-5879 (electronic)",
  ISSN-L =       "1751-1577",
  bibdate =      "Wed Sep 9 16:29:53 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1751157715200181",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Informetrics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/17511577/",
}


bibtype: article
filename: pagerank.bib
label: Peng:2015:IPC
author: Wei Peng and Jianxin Wang and Bihai Zhao and Lusheng Wang
journal: j-TCBB
volume: 12
number: 1
pages: 179--192
month: jan
monthnumber: 1
year: 2015
CODEN: ITCBCY
DOI: https://doi.org/10.1109/TCBB.2014.2343954
ISSN: 1545-5963 (print), 1557-9964 (electronic) OR 15455963 (print), 15579964 (electronic)
ISSNL: 1545-5963 OR 15455963
bibdate: Fri Aug 28 05:40:09 MDT 2015
bibtimestamp: 2015.08.28 05:40:09 MDT
fjournal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J954
rawauthor: Wei Peng and Jianxin Wang and Bihai Zhao and Lusheng Wang
title:
Identification of protein complexes using weighted PageRank--Nibble algorithm and core-attachment structure

bibsource:
http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tcbb.bib

abstract:
Protein complexes play a significant role in understanding the underlying mechanism of most cellular functions. Recently, many researchers have explored computational methods to identify protein complexes from protein-protein interaction (PPI) networks. One group of researchers focus on detecting local dense subgraphs which correspond to protein complexes by considering local neighbors. The drawback of this kind of approach is that the global information of the networks is ignored. Some methods such as Markov Clustering algorithm (MCL), PageRank--Nibble are proposed to find protein complexes based on random walk technique which can exploit the global structure of networks. However, these methods ignore the inherent core-attachment structure of protein complexes and treat adjacent node equally. In this paper, we design a weighted PageRank--Nibble algorithm which assigns each adjacent node with different probability, and propose a novel method named WPNCA to detect protein complex from PPI networks by using weighted PageRank--Nibble algorithm and core-attachment structure. Firstly, WPNCA partitions the PPI networks into multiple dense clusters by using weighted PageRank--Nibble algorithm. Then the cores of these clusters are detected and the rest of proteins in the clusters will be selected as attachments to form the final predicted protein complexes. The experiments on yeast data show that WPNCA outperforms the existing methods in terms of both accuracy and p-value. The software for WPNCA is available at ``http://netlab.csu.edu.cn/bioinfomatics/weipeng/WPNCA/download.html''

rawabstract:
Protein complexes play a significant role in understanding the underlying mechanism of most cellular functions. Recently, many researchers have explored computational methods to identify protein complexes from protein-protein interaction (PPI) networks. One group of researchers focus on detecting local dense subgraphs which correspond to protein complexes by considering local neighbors. The drawback of this kind of approach is that the global information of the networks is ignored. Some methods such as Markov Clustering algorithm (MCL), PageRank--Nibble are proposed to find protein complexes based on random walk technique which can exploit the global structure of networks. However, these methods ignore the inherent core-attachment structure of protein complexes and treat adjacent node equally. In this paper, we design a weighted PageRank--Nibble algorithm which assigns each adjacent node with different probability, and propose a novel method named WPNCA to detect protein complex from PPI networks by using weighted PageRank--Nibble algorithm and core-attachment structure. Firstly, WPNCA partitions the PPI networks into multiple dense clusters by using weighted PageRank--Nibble algorithm. Then the cores of these clusters are detected and the rest of proteins in the clusters will be selected as attachments to form the final predicted protein complexes. The experiments on yeast data show that WPNCA outperforms the existing methods in terms of both accuracy and p-value. The software for WPNCA is available at ``http://netlab.csu.edu.cn/bioinfomatics/weipeng/WPNCA/download.html''

rawtitle:
Identification of protein complexes using weighted {PageRank--Nibble} algorithm and core-attachment structure

entry:
@Article{Peng:2015:IPC,
  author =       "Wei Peng and Jianxin Wang and Bihai Zhao and Lusheng
                 Wang",
  title =        "Identification of protein complexes using weighted
                 {PageRank--Nibble} algorithm and core-attachment
                 structure",
  journal =      j-TCBB,
  volume =       "12",
  number =       "1",
  pages =        "179--192",
  month =        jan,
  year =         "2015",
  CODEN =        "ITCBCY",
  DOI =          "https://doi.org/10.1109/TCBB.2014.2343954",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Fri Aug 28 05:40:09 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  abstract =     "Protein complexes play a significant role in
                 understanding the underlying mechanism of most cellular
                 functions. Recently, many researchers have explored
                 computational methods to identify protein complexes
                 from protein-protein interaction (PPI) networks. One
                 group of researchers focus on detecting local dense
                 subgraphs which correspond to protein complexes by
                 considering local neighbors. The drawback of this kind
                 of approach is that the global information of the
                 networks is ignored. Some methods such as Markov
                 Clustering algorithm (MCL), PageRank--Nibble are
                 proposed to find protein complexes based on random walk
                 technique which can exploit the global structure of
                 networks. However, these methods ignore the inherent
                 core-attachment structure of protein complexes and
                 treat adjacent node equally. In this paper, we design a
                 weighted PageRank--Nibble algorithm which assigns each
                 adjacent node with different probability, and propose a
                 novel method named WPNCA to detect protein complex from
                 PPI networks by using weighted PageRank--Nibble
                 algorithm and core-attachment structure. Firstly, WPNCA
                 partitions the PPI networks into multiple dense
                 clusters by using weighted PageRank--Nibble algorithm.
                 Then the cores of these clusters are detected and the
                 rest of proteins in the clusters will be selected as
                 attachments to form the final predicted protein
                 complexes. The experiments on yeast data show that
                 WPNCA outperforms the existing methods in terms of both
                 accuracy and p-value. The software for WPNCA is
                 available at
                 ``http://netlab.csu.edu.cn/bioinfomatics/weipeng/WPNCA/download.html''",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J954",
}


bibtype: article
filename: pagerank.bib
label: Pop:2015:AMS
author: Florin Pop and Radu-Ioan Ciobanu and Ciprian Dobre
title: Adaptive method to support social-based mobile networks using a PageRank approach
journal: j-CCPE
volume: 27
number: 8
pages: 1900--1912
day: 10
month: jun
monthnumber: 6
year: 2015
CODEN: CCPEBO
DOI: https://doi.org/10.1002/cpe.3103
ISSN: 1532-0626 (print), 1532-0634 (electronic) OR 15320626 (print), 15320634 (electronic)
ISSNL: 1532-0626 OR 15320626
bibdate: Sat Jul 25 19:54:07 MDT 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/ccpe.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.07.25 19:54:07 MDT
fjournal: Concurrency and Computation: Practice and Experience
acknowledgement: ack-nhfb
journalURL: http://www.interscience.wiley.com/jpages/1532-0626
onlinedate: 23 Jul 2013
rawauthor: Florin Pop and Radu-Ioan Ciobanu and Ciprian Dobre
rawtitle: Adaptive method to support social-based mobile networks using a {PageRank} approach
entry:
@Article{Pop:2015:AMS,
  author =       "Florin Pop and Radu-Ioan Ciobanu and Ciprian Dobre",
  title =        "Adaptive method to support social-based mobile
                 networks using a {PageRank} approach",
  journal =      j-CCPE,
  volume =       "27",
  number =       "8",
  pages =        "1900--1912",
  day =          "10",
  month =        jun,
  year =         "2015",
  CODEN =        "CCPEBO",
  DOI =          "https://doi.org/10.1002/cpe.3103",
  ISSN =         "1532-0626 (print), 1532-0634 (electronic)",
  ISSN-L =       "1532-0626",
  bibdate =      "Sat Jul 25 19:54:07 MDT 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ccpe.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Concurrency and Computation: Practice and Experience",
  journal-URL =  "http://www.interscience.wiley.com/jpages/1532-0626",
  onlinedate =   "23 Jul 2013",
}


bibtype: article
filename: pagerank.bib
label: Sarma:2015:FDP
author: Atish Das Sarma and Anisur Rahaman Molla and Gopal Pandurangan and Eli Upfal
title: Fast distributed PageRank computation
journal: j-THEOR-COMP-SCI
volume: 561 (part B)
pages: 113--121
day: 4
month: jan
monthnumber: 1
year: 2015
CODEN: TCSCDI
ISSN: 0304-3975 (print), 1879-2294 (electronic) OR 03043975 (print), 18792294 (electronic)
ISSNL: 0304-3975 OR 03043975
bibdate: Tue Dec 2 19:05:34 MST 2014
bibtimestamp: 2014.12.02 19:05:34 MST
URL: http://www.sciencedirect.com/science/article/pii/S0304397514002709
fjournal: Theoretical Computer Science
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03043975/
rawauthor: Atish Das Sarma and Anisur Rahaman Molla and Gopal Pandurangan and Eli Upfal
rawtitle: Fast distributed {PageRank} computation
bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tcs2010.bib

entry:
@Article{Sarma:2015:FDP,
  author =       "Atish Das Sarma and Anisur Rahaman Molla and Gopal
                 Pandurangan and Eli Upfal",
  title =        "Fast distributed {PageRank} computation",
  journal =      j-THEOR-COMP-SCI,
  volume =       "561 (part B)",
  number =       "??",
  pages =        "113--121",
  day =          "4",
  month =        jan,
  year =         "2015",
  CODEN =        "TCSCDI",
  ISSN =         "0304-3975 (print), 1879-2294 (electronic)",
  ISSN-L =       "0304-3975",
  bibdate =      "Tue Dec 2 19:05:34 MST 2014",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tcs2010.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0304397514002709",
  acknowledgement = ack-nhfb,
  fjournal =     "Theoretical Computer Science",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03043975/",
}


bibtype: article
filename: pagerank.bib
label: Zhu:2015:SAP
author: Fanwei Zhu and Yuan Fang and Kevin Chen-Chuan Chang and Jing Ying
title: Scheduled approximation for Personalized PageRank with Utility-based Hub Selection
journal: j-VLDB-J
volume: 24
number: 5
pages: 655--679
month: oct
monthnumber: 10
year: 2015
CODEN: VLDBFR
DOI: https://doi.org/10.1007/s00778-014-0376-8
ISSN: 1066-8888 (print), 0949-877X (electronic) OR 10668888 (print), 0949877X (electronic)
ISSNL: 1066-8888 OR 10668888
bibdate: Fri Sep 18 06:51:09 MDT 2015
bibtimestamp: 2015.09.18 06:51:09 MDT
fjournal: VLDB Journal: Very Large Data Bases
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/toc.cfm?id=J869
rawauthor: Fanwei Zhu and Yuan Fang and Kevin Chen-Chuan Chang and Jing Ying
rawtitle: Scheduled approximation for {Personalized PageRank} with {Utility-based Hub Selection}
bibsource:
http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbj.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. In addition, 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. Given the key roles played by the hubs, we further investigate the problem of hub selection. In particular, we develop a conceptual model to select hubs based on the two desirable properties of hubs--sharing and discriminating, and present several different strategies to realize the conceptual model. 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 scales well on larger graphs. In particular, we are able to achieve near-constant time online query processing irrespective of graph size.

rawabstract:
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. In addition, 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. Given the key roles played by the hubs, we further investigate the problem of hub selection. In particular, we develop a conceptual model to select hubs based on the two desirable properties of hubs--sharing and discriminating, and present several different strategies to realize the conceptual model. 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 scales well on larger graphs. In particular, we are able to achieve near-constant time online query processing irrespective of graph size.

entry:
@Article{Zhu:2015:SAP,
  author =       "Fanwei Zhu and Yuan Fang and Kevin Chen-Chuan Chang
                 and Jing Ying",
  title =        "Scheduled approximation for {Personalized PageRank}
                 with {Utility-based Hub Selection}",
  journal =      j-VLDB-J,
  volume =       "24",
  number =       "5",
  pages =        "655--679",
  month =        oct,
  year =         "2015",
  CODEN =        "VLDBFR",
  DOI =          "https://doi.org/10.1007/s00778-014-0376-8",
  ISSN =         "1066-8888 (print), 0949-877X (electronic)",
  ISSN-L =       "1066-8888",
  bibdate =      "Fri Sep 18 06:51:09 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbj.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. In addition, 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. Given the
                 key roles played by the hubs, we further investigate
                 the problem of hub selection. In particular, we develop
                 a conceptual model to select hubs based on the two
                 desirable properties of hubs--sharing and
                 discriminating, and present several different
                 strategies to realize the conceptual model. 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 scales 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 =     "VLDB Journal: Very Large Data Bases",
  journal-URL =  "http://portal.acm.org/toc.cfm?id=J869",
}


bibtype: article
filename: pagerank.bib
label: Agryzkov:2016:NHN
author: Taras Agryzkov and Leandro Tortosa and Jose F. Vicent
journal: j-APPL-MATH-COMP
volume: 291
pages: 14--29
day: 1
month: dec
monthnumber: 12
year: 2016
CODEN: AMHCBQ
ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
bibdate: Wed Sep 28 06:57:06 MDT 2016
bibtimestamp: 2016.09.28 06:57:06 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0096300316304076
fjournal: Applied Mathematics and Computation
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00963003/
rawauthor: Taras Agryzkov and Leandro Tortosa and Jose F. Vicent
title:
New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks

bibsource:
https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

rawtitle:
New highlights and a new centrality measure based on the {Adapted PageRank Algorithm} for urban networks

entry:
@Article{Agryzkov:2016:NHN,
  author =       "Taras Agryzkov and Leandro Tortosa and Jose F.
                 Vicent",
  title =        "New highlights and a new centrality measure based on
                 the {Adapted PageRank Algorithm} for urban networks",
  journal =      j-APPL-MATH-COMP,
  volume =       "291",
  number =       "??",
  pages =        "14--29",
  day =          "1",
  month =        dec,
  year =         "2016",
  CODEN =        "AMHCBQ",
  ISSN =         "0096-3003 (print), 1873-5649 (electronic)",
  ISSN-L =       "0096-3003",
  bibdate =      "Wed Sep 28 06:57:06 MDT 2016",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0096300316304076",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Mathematics and Computation",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00963003/",
}


bibtype: book
filename: pagerank.bib
label: Arbenz:2016:LNS
author: Peter Arbenz
title: Lecture Notes on Solving Large Scale Eigenvalue Problems
publisher: Computer Science Department, ETH Zurich
address: Zurich, Switzerland
pages: vi + 259
year: 2016
bibdate: Mon Sep 04 10:05:42 2023
bibtimestamp: 2023.09.04 10:05:42 ???
URL: https://people.inf.ethz.ch/arbenz/ewp/Lnotes/lsevp.pdf
acknowledgement: ack-nhfb
rawauthor: Peter Arbenz
rawtitle: Lecture Notes on Solving Large Scale Eigenvalue Problems
bibsource:
https://www.math.utah.edu/pub/bibnet/authors/h/hartree-douglas-r.bib; https://www.math.utah.edu/pub/bibnet/authors/l/lanczos-cornelius.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

TOC:
1 Introduction / 1 1.1 What makes eigenvalues interesting? / 1 1.2 Example 1: The vibrating string / 2 1.2.1 Problem setting / 2 1.2.2 The method of separation of variables / 5 1.3 Numerical methods for solving 1-dimensional problems / 6 1.3.1 Finite differences / 6 1.3.2 The finite element method / 7 1.3.3 Global functions / 8 1.3.4 A numerical comparison / 9 1.4 Example 2: The heat equation / 9 1.5 Example 3: The wave equation / 12 1.6 The 2D Laplace eigenvalue problem / 13 1.6.1 The finite difference method / 13 1.6.2 The finite element method (FEM) / 16 1.6.3 A numerical example / 20 1.7 Cavity resonances in particle accelerators / 21 1.8 Spectral clustering / 23 1.8.1 The graph Laplacian / 24 1.8.2 Spectral clustering / 25 1.8.3 Normalized graph Laplacians / 27 1.9 Google's PageRank / 28 1.10 Other sources of eigenvalue problems / 30 Bibliography / 31 2 Basics / 33 2.1 Notation / 33 2.2 Statement of the problem / 34 2.3 Similarity transformations / 37 2.4 Schur decomposition / 38 2.5 The real Schur decomposition / 39 2.6 Normal matrices / 40 2.7 Hermitian matrices / 41 2.8 The Jordan normal form / 43 2.9 Projections / 45 2.10 The Rayleigh quotient / 47 2.11 Cholesky factorization / 49 2.12 The singular value decomposition (SVD) / 50 Bibliography / 52 3 Newton methods / 53 3.1 Linear and nonlinear eigenvalue problems / 53 3.2 Zeros of the determinant / 54 3.2.1 Algorithmic differentiation / 55 3.2.2 Hyman's algorithm / 55 3.2.3 Computing multiple zeros / 58 3.3 Newton methods for the constrained matrix problem / 58 3.4 Successive linear approximations / 60 Bibliography / 61 4 The $ Q R $ Algorithm / 63 4.1 The basic $ Q R $ algorithm / 63 4.1.1 Numerical experiments / 64 4.2 The Hessenberg $ Q R $ algorithm / 67 4.2.1 A numerical experiment / 69 4.2.2 Complexity / 70 4.3 The Householder reduction to Hessenberg form / 71 4.3.1 Householder reflectors / 71 4.3.2 Reduction to Hessenberg form / 71 4.4 Improving the convergence of the $ Q R $ algorithm / 73 4.4.1 A numerical example / 74 4.4.2 $ Q R $ algorithm with shifts / 75 4.4.3 A numerical example / 76 4.5 The double shift $ Q R $ algorithm / 77 4.5.1 A numerical example / 81 4.5.2 The complexity / 83 4.6 The symmetric tridiagonal $ Q R $ algorithm / 84 4.6.1 Reduction to tridiagonal form / 84 4.6.2 The tridiagonal $ Q R $ algorithm / 85 4.7 Research / 87 4.8 Summary / 87 Bibliography / 88 5 Cuppen's Divide and Conquer Algorithm / 91 5.1 The divide and conquer idea / 91 5.2 Partitioning the tridiagonal matrix / 92 5.3 Solving the small systems / 92 5.4 Deflation / 93 5.4.1 Numerical examples / 94 5.5 The eigenvalue problem for $D + rho v v^T$ / 95 5.6 Solving the secular equation / 98 5.7 A first algorithm / 99 5.7.1 A numerical example / 100 5.8 The algorithm of Gu and Eisenstat / 103 5.8.1 A numerical example [continued] / 104 Bibliography / 107 6 LAPACK and the BLAS / 109 6.1 LAPACK / 109 6.2 BLAS / 110 6.2.1 Typical performance numbers for the BLAS / 111 6.3 Blocking / 113 6.4 LAPACK solvers for the symmetric eigenproblems / 114 6.5 Generalized Symmetric Definite Eigenproblems (GSEP) / 116 6.6 An example of a LAPACK routines / 116 Bibliography / 122 7 Vector iteration (power method) / 125 7.1 Simple vector iteration / 125 7.2 Angles between vectors / 126 7.3 Convergence analysis / 127 7.4 A numerical example / 130 7.5 The symmetric case / 131 7.6 Inverse vector iteration / 135 7.7 The generalized eigenvalue problem / 139 7.8 Computing higher eigenvalues / 139 7.9 Rayleigh quotient iteration / 140 7.9.1 A numerical example / 143 Bibliography / 144 8 Simultaneous vector or subspace iterations / 145 8.1 Basic subspace iteration / 145 8.2 Angles between subspaces / 146 8.3 Convergence of basic subspace iteration / 148 8.4 Accelerating subspace iteration / 153 8.5 Relation between subspace iteration and $ Q R $ algorithm / 158 8.6 Addendum / 161 Bibliography / 161 9 Krylov subspaces / 163 9.1 Introduction / 163 9.2 Definition and basic properties / 164 9.3 Polynomial representation of Krylov subspaces / 165 9.4 Error bounds of Saad / 168 Bibliography / 171 10 Arnoldi and Lanczos algorithms / 173 10.1 An orthonormal basis for the Krylov space Kj (x) / 173 10.2 Arnoldi algorithm with explicit restarts / 175 10.3 The Lanczos basis / 176 10.4 The Lanczos process as an iterative method / 178 10.5 An error analysis of the unmodified Lanczos algorithm / 185 10.6 Partial reorthogonalization / 187 10.7 Block Lanczos / 190 10.8 External selective reorthogonalization / 193 Bibliography / 194 11 Restarting Arnoldi and Lanczos algorithms / 195 11.1 The $m$-step Arnoldi iteration / 195 11.2 Implicit restart / 196 11.3 Convergence criterion / 198 11.4 The generalized eigenvalue problem / 199 11.5 A numerical example / 201 11.6 Another numerical example / 206 11.7 The Lanczos algorithm with thick restarts / 210 11.8 Krylov--Schur algorithm / 213 11.9 The rational Krylov space method / 214 Bibliography / 215 12 The Jacobi--Davidson Method / 217 12.1 The Davidson algorithm / 217 12.2 The Jacobi orthogonal component correction / 218 12.2.1 Restarts / 221 12.2.2 The computation of several eigenvalues / 221 12.2.3 Spectral shifts / 222 12.3 The generalized Hermitian eigenvalue problem / 224 12.4 A numerical example / 224 12.5 The Jacobi--Davidson algorithm for interior eigenvalues / 228 12.6 Harmonic Ritz values and vectors / 229 12.7 Refined Ritz vectors / 231 12.8 The generalized Schur decomposition / 233 12.9 JDQZ: Computing a partial $ Q Z $ decomposition / 233 12.9.1 Restart / 235 12.9.2 Deflation / 235 12.9.3 Algorithm / 236 12.10 Jacobi--Davidson for nonlinear eigenvalue problems / 236 Bibliography / 239 13 Rayleigh quotient and trace minimization / 241 13.1 Introduction / 241 13.2 The method of steepest descent / 242 13.3 The conjugate gradient algorithm / 243 13.4 Locally optimal PCG (LOPCG) / 247 13.5 The block Rayleigh quotient minimization algorithm (BRQMIN) / 250 13.6 The locally-optimal block preconditioned conjugate gradient method (LOBPCG) / 250 13.7 A numerical example / 251 13.8 Trace minimization / 253 Bibliography / 258

rawTOC:
1 Introduction / 1 \\ 1.1 What makes eigenvalues interesting? / 1 \\ 1.2 Example 1: The vibrating string / 2 \\ 1.2.1 Problem setting / 2 \\ 1.2.2 The method of separation of variables / 5 \\ 1.3 Numerical methods for solving 1-dimensional problems / 6 \\ 1.3.1 Finite differences / 6 \\ 1.3.2 The finite element method / 7 \\ 1.3.3 Global functions / 8 \\ 1.3.4 A numerical comparison / 9 \\ 1.4 Example 2: The heat equation / 9 \\ 1.5 Example 3: The wave equation / 12 \\ 1.6 The 2D Laplace eigenvalue problem / 13 \\ 1.6.1 The finite difference method / 13 \\ 1.6.2 The finite element method (FEM) / 16 \\ 1.6.3 A numerical example / 20 \\ 1.7 Cavity resonances in particle accelerators / 21 \\ 1.8 Spectral clustering / 23 \\ 1.8.1 The graph Laplacian / 24 \\ 1.8.2 Spectral clustering / 25 \\ 1.8.3 Normalized graph Laplacians / 27 \\ 1.9 Google's PageRank / 28 \\ 1.10 Other sources of eigenvalue problems / 30 \\ Bibliography / 31 \\ 2 Basics / 33 \\ 2.1 Notation / 33 \\ 2.2 Statement of the problem / 34 \\ 2.3 Similarity transformations / 37 \\ 2.4 Schur decomposition / 38 \\ 2.5 The real Schur decomposition / 39 \\ 2.6 Normal matrices / 40 \\ 2.7 Hermitian matrices / 41 \\ 2.8 The Jordan normal form / 43 \\ 2.9 Projections / 45 \\ 2.10 The Rayleigh quotient / 47 \\ 2.11 Cholesky factorization / 49 \\ 2.12 The singular value decomposition (SVD) / 50 \\ Bibliography / 52 \\ 3 Newton methods / 53 \\ 3.1 Linear and nonlinear eigenvalue problems / 53 \\ 3.2 Zeros of the determinant / 54 \\ 3.2.1 Algorithmic differentiation / 55 \\ 3.2.2 Hyman's algorithm / 55 \\ 3.2.3 Computing multiple zeros / 58 \\ 3.3 Newton methods for the constrained matrix problem / 58 \\ 3.4 Successive linear approximations / 60 \\ Bibliography / 61 \\ 4 The $ Q R $ Algorithm / 63 \\ 4.1 The basic $ Q R $ algorithm / 63 \\ 4.1.1 Numerical experiments / 64 \\ 4.2 The Hessenberg $ Q R $ algorithm / 67 \\ 4.2.1 A numerical experiment / 69 \\ 4.2.2 Complexity / 70 \\ 4.3 The Householder reduction to Hessenberg form / 71 \\ 4.3.1 Householder reflectors / 71 \\ 4.3.2 Reduction to Hessenberg form / 71 \\ 4.4 Improving the convergence of the $ Q R $ algorithm / 73 \\ 4.4.1 A numerical example / 74 \\ 4.4.2 $ Q R $ algorithm with shifts / 75 \\ 4.4.3 A numerical example / 76 \\ 4.5 The double shift $ Q R $ algorithm / 77 \\ 4.5.1 A numerical example / 81 \\ 4.5.2 The complexity / 83 \\ 4.6 The symmetric tridiagonal $ Q R $ algorithm / 84 \\ 4.6.1 Reduction to tridiagonal form / 84 \\ 4.6.2 The tridiagonal $ Q R $ algorithm / 85 \\ 4.7 Research / 87 \\ 4.8 Summary / 87 \\ Bibliography / 88 \\ 5 Cuppen's Divide and Conquer Algorithm / 91 \\ 5.1 The divide and conquer idea / 91 \\ 5.2 Partitioning the tridiagonal matrix / 92 \\ 5.3 Solving the small systems / 92 \\ 5.4 Deflation / 93 \\ 5.4.1 Numerical examples / 94 \\ 5.5 The eigenvalue problem for $D + \rho v v^T$ / 95 \\ 5.6 Solving the secular equation / 98 \\ 5.7 A first algorithm / 99 \\ 5.7.1 A numerical example / 100 \\ 5.8 The algorithm of Gu and Eisenstat / 103 \\ 5.8.1 A numerical example [continued] / 104 \\ Bibliography / 107 \\ 6 LAPACK and the BLAS / 109 \\ 6.1 LAPACK / 109 \\ 6.2 BLAS / 110 \\ 6.2.1 Typical performance numbers for the BLAS / 111 \\ 6.3 Blocking / 113 \\ 6.4 LAPACK solvers for the symmetric eigenproblems / 114 \\ 6.5 Generalized Symmetric Definite Eigenproblems (GSEP) / 116 \\ 6.6 An example of a LAPACK routines / 116 \\ Bibliography / 122 \\ 7 Vector iteration (power method) / 125 \\ 7.1 Simple vector iteration / 125 \\ 7.2 Angles between vectors / 126 \\ 7.3 Convergence analysis / 127 \\ 7.4 A numerical example / 130 \\ 7.5 The symmetric case / 131 \\ 7.6 Inverse vector iteration / 135 \\ 7.7 The generalized eigenvalue problem / 139 \\ 7.8 Computing higher eigenvalues / 139 \\ 7.9 Rayleigh quotient iteration / 140 \\ 7.9.1 A numerical example / 143 \\ Bibliography / 144 \\ 8 Simultaneous vector or subspace iterations / 145 \\ 8.1 Basic subspace iteration / 145 \\ 8.2 Angles between subspaces / 146 \\ 8.3 Convergence of basic subspace iteration / 148 \\ 8.4 Accelerating subspace iteration / 153 \\ 8.5 Relation between subspace iteration and $ Q R $ algorithm / 158 \\ 8.6 Addendum / 161 \\ Bibliography / 161 \\ 9 Krylov subspaces / 163 \\ 9.1 Introduction / 163 \\ 9.2 Definition and basic properties / 164 \\ 9.3 Polynomial representation of Krylov subspaces / 165 \\ 9.4 Error bounds of Saad / 168 \\ Bibliography / 171 \\ 10 Arnoldi and Lanczos algorithms / 173 \\ 10.1 An orthonormal basis for the Krylov space Kj (x) / 173 \\ 10.2 Arnoldi algorithm with explicit restarts / 175 \\ 10.3 The Lanczos basis / 176 \\ 10.4 The Lanczos process as an iterative method / 178 \\ 10.5 An error analysis of the unmodified Lanczos algorithm / 185 \\ 10.6 Partial reorthogonalization / 187 \\ 10.7 Block Lanczos / 190 \\ 10.8 External selective reorthogonalization / 193 \\ Bibliography / 194 \\ 11 Restarting Arnoldi and Lanczos algorithms / 195 \\ 11.1 The $m$-step Arnoldi iteration / 195 \\ 11.2 Implicit restart / 196 \\ 11.3 Convergence criterion / 198 \\ 11.4 The generalized eigenvalue problem / 199 \\ 11.5 A numerical example / 201 \\ 11.6 Another numerical example / 206 \\ 11.7 The Lanczos algorithm with thick restarts / 210 \\ 11.8 Krylov--Schur algorithm / 213 \\ 11.9 The rational Krylov space method / 214 \\ Bibliography / 215 \\ 12 The Jacobi--Davidson Method / 217 \\ 12.1 The Davidson algorithm / 217 \\ 12.2 The Jacobi orthogonal component correction / 218 \\ 12.2.1 Restarts / 221 \\ 12.2.2 The computation of several eigenvalues / 221 \\ 12.2.3 Spectral shifts / 222 \\ 12.3 The generalized Hermitian eigenvalue problem / 224 \\ 12.4 A numerical example / 224 \\ 12.5 The Jacobi--Davidson algorithm for interior eigenvalues / 228 \\ 12.6 Harmonic Ritz values and vectors / 229 \\ 12.7 Refined Ritz vectors / 231 \\ 12.8 The generalized Schur decomposition / 233 \\ 12.9 JDQZ: Computing a partial $ Q Z $ decomposition / 233 \\ 12.9.1 Restart / 235 \\ 12.9.2 Deflation / 235 \\ 12.9.3 Algorithm / 236 \\ 12.10 Jacobi--Davidson for nonlinear eigenvalue problems / 236 \\ Bibliography / 239 \\ 13 Rayleigh quotient and trace minimization / 241 \\ 13.1 Introduction / 241 \\ 13.2 The method of steepest descent / 242 \\ 13.3 The conjugate gradient algorithm / 243 \\ 13.4 Locally optimal PCG (LOPCG) / 247 \\ 13.5 The block Rayleigh quotient minimization algorithm (BRQMIN) / 250 \\ 13.6 The locally-optimal block preconditioned conjugate gradient method (LOBPCG) / 250 \\ 13.7 A numerical example / 251 \\ 13.8 Trace minimization / 253 \\ Bibliography / 258

entry:
@Book{Arbenz:2016:LNS,
  author =       "Peter Arbenz",
  title =        "Lecture Notes on Solving Large Scale Eigenvalue
                 Problems",
  publisher =    "Computer Science Department, ETH Z{\"u}rich",
  address =      "Z{\"u}rich, Switzerland",
  pages =        "vi + 259",
  year =         "2016",
  bibdate =      "Mon Sep 04 10:05:42 2023",
  bibsource =    "https://www.math.utah.edu/pub/bibnet/authors/h/hartree-douglas-r.bib;
                 https://www.math.utah.edu/pub/bibnet/authors/l/lanczos-cornelius.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://people.inf.ethz.ch/arbenz/ewp/Lnotes/lsevp.pdf",
  acknowledgement = ack-nhfb,
  tableofcontents = "1 Introduction / 1 \\
                 1.1 What makes eigenvalues interesting? / 1 \\
                 1.2 Example 1: The vibrating string / 2 \\
                 1.2.1 Problem setting / 2 \\
                 1.2.2 The method of separation of variables / 5 \\
                 1.3 Numerical methods for solving 1-dimensional
                 problems / 6 \\
                 1.3.1 Finite differences / 6 \\
                 1.3.2 The finite element method / 7 \\
                 1.3.3 Global functions / 8 \\
                 1.3.4 A numerical comparison / 9 \\
                 1.4 Example 2: The heat equation / 9 \\
                 1.5 Example 3: The wave equation / 12 \\
                 1.6 The 2D Laplace eigenvalue problem / 13 \\
                 1.6.1 The finite difference method / 13 \\
                 1.6.2 The finite element method (FEM) / 16 \\
                 1.6.3 A numerical example / 20 \\
                 1.7 Cavity resonances in particle accelerators / 21 \\
                 1.8 Spectral clustering / 23 \\
                 1.8.1 The graph Laplacian / 24 \\
                 1.8.2 Spectral clustering / 25 \\
                 1.8.3 Normalized graph Laplacians / 27 \\
                 1.9 Google's PageRank / 28 \\
                 1.10 Other sources of eigenvalue problems / 30 \\
                 Bibliography / 31 \\
                 2 Basics / 33 \\
                 2.1 Notation / 33 \\
                 2.2 Statement of the problem / 34 \\
                 2.3 Similarity transformations / 37 \\
                 2.4 Schur decomposition / 38 \\
                 2.5 The real Schur decomposition / 39 \\
                 2.6 Normal matrices / 40 \\
                 2.7 Hermitian matrices / 41 \\
                 2.8 The Jordan normal form / 43 \\
                 2.9 Projections / 45 \\
                 2.10 The Rayleigh quotient / 47 \\
                 2.11 Cholesky factorization / 49 \\
                 2.12 The singular value decomposition (SVD) / 50 \\
                 Bibliography / 52 \\
                 3 Newton methods / 53 \\
                 3.1 Linear and nonlinear eigenvalue problems / 53 \\
                 3.2 Zeros of the determinant / 54 \\
                 3.2.1 Algorithmic differentiation / 55 \\
                 3.2.2 Hyman's algorithm / 55 \\
                 3.2.3 Computing multiple zeros / 58 \\
                 3.3 Newton methods for the constrained matrix problem /
                 58 \\
                 3.4 Successive linear approximations / 60 \\
                 Bibliography / 61 \\
                 4 The $ Q R $ Algorithm / 63 \\
                 4.1 The basic $ Q R $ algorithm / 63 \\
                 4.1.1 Numerical experiments / 64 \\
                 4.2 The Hessenberg $ Q R $ algorithm / 67 \\
                 4.2.1 A numerical experiment / 69 \\
                 4.2.2 Complexity / 70 \\
                 4.3 The Householder reduction to Hessenberg form / 71
                 \\
                 4.3.1 Householder reflectors / 71 \\
                 4.3.2 Reduction to Hessenberg form / 71 \\
                 4.4 Improving the convergence of the $ Q R $ algorithm
                 / 73 \\
                 4.4.1 A numerical example / 74 \\
                 4.4.2 $ Q R $ algorithm with shifts / 75 \\
                 4.4.3 A numerical example / 76 \\
                 4.5 The double shift $ Q R $ algorithm / 77 \\
                 4.5.1 A numerical example / 81 \\
                 4.5.2 The complexity / 83 \\
                 4.6 The symmetric tridiagonal $ Q R $ algorithm / 84
                 \\
                 4.6.1 Reduction to tridiagonal form / 84 \\
                 4.6.2 The tridiagonal $ Q R $ algorithm / 85 \\
                 4.7 Research / 87 \\
                 4.8 Summary / 87 \\
                 Bibliography / 88 \\
                 5 Cuppen's Divide and Conquer Algorithm / 91 \\
                 5.1 The divide and conquer idea / 91 \\
                 5.2 Partitioning the tridiagonal matrix / 92 \\
                 5.3 Solving the small systems / 92 \\
                 5.4 Deflation / 93 \\
                 5.4.1 Numerical examples / 94 \\
                 5.5 The eigenvalue problem for $D + \rho v v^T$ / 95
                 \\
                 5.6 Solving the secular equation / 98 \\
                 5.7 A first algorithm / 99 \\
                 5.7.1 A numerical example / 100 \\
                 5.8 The algorithm of Gu and Eisenstat / 103 \\
                 5.8.1 A numerical example [continued] / 104 \\
                 Bibliography / 107 \\
                 6 LAPACK and the BLAS / 109 \\
                 6.1 LAPACK / 109 \\
                 6.2 BLAS / 110 \\
                 6.2.1 Typical performance numbers for the BLAS / 111
                 \\
                 6.3 Blocking / 113 \\
                 6.4 LAPACK solvers for the symmetric eigenproblems /
                 114 \\
                 6.5 Generalized Symmetric Definite Eigenproblems (GSEP)
                 / 116 \\
                 6.6 An example of a LAPACK routines / 116 \\
                 Bibliography / 122 \\
                 7 Vector iteration (power method) / 125 \\
                 7.1 Simple vector iteration / 125 \\
                 7.2 Angles between vectors / 126 \\
                 7.3 Convergence analysis / 127 \\
                 7.4 A numerical example / 130 \\
                 7.5 The symmetric case / 131 \\
                 7.6 Inverse vector iteration / 135 \\
                 7.7 The generalized eigenvalue problem / 139 \\
                 7.8 Computing higher eigenvalues / 139 \\
                 7.9 Rayleigh quotient iteration / 140 \\
                 7.9.1 A numerical example / 143 \\
                 Bibliography / 144 \\
                 8 Simultaneous vector or subspace iterations / 145 \\
                 8.1 Basic subspace iteration / 145 \\
                 8.2 Angles between subspaces / 146 \\
                 8.3 Convergence of basic subspace iteration / 148 \\
                 8.4 Accelerating subspace iteration / 153 \\
                 8.5 Relation between subspace iteration and $ Q R $
                 algorithm / 158 \\
                 8.6 Addendum / 161 \\
                 Bibliography / 161 \\
                 9 Krylov subspaces / 163 \\
                 9.1 Introduction / 163 \\
                 9.2 Definition and basic properties / 164 \\
                 9.3 Polynomial representation of Krylov subspaces / 165
                 \\
                 9.4 Error bounds of Saad / 168 \\
                 Bibliography / 171 \\
                 10 Arnoldi and Lanczos algorithms / 173 \\
                 10.1 An orthonormal basis for the Krylov space Kj (x) /
                 173 \\
                 10.2 Arnoldi algorithm with explicit restarts / 175 \\
                 10.3 The Lanczos basis / 176 \\
                 10.4 The Lanczos process as an iterative method / 178
                 \\
                 10.5 An error analysis of the unmodified Lanczos
                 algorithm / 185 \\
                 10.6 Partial reorthogonalization / 187 \\
                 10.7 Block Lanczos / 190 \\
                 10.8 External selective reorthogonalization / 193 \\
                 Bibliography / 194 \\
                 11 Restarting Arnoldi and Lanczos algorithms / 195 \\
                 11.1 The $m$-step Arnoldi iteration / 195 \\
                 11.2 Implicit restart / 196 \\
                 11.3 Convergence criterion / 198 \\
                 11.4 The generalized eigenvalue problem / 199 \\
                 11.5 A numerical example / 201 \\
                 11.6 Another numerical example / 206 \\
                 11.7 The Lanczos algorithm with thick restarts / 210
                 \\
                 11.8 Krylov--Schur algorithm / 213 \\
                 11.9 The rational Krylov space method / 214 \\
                 Bibliography / 215 \\
                 12 The Jacobi--Davidson Method / 217 \\
                 12.1 The Davidson algorithm / 217 \\
                 12.2 The Jacobi orthogonal component correction / 218
                 \\
                 12.2.1 Restarts / 221 \\
                 12.2.2 The computation of several eigenvalues / 221 \\
                 12.2.3 Spectral shifts / 222 \\
                 12.3 The generalized Hermitian eigenvalue problem / 224
                 \\
                 12.4 A numerical example / 224 \\
                 12.5 The Jacobi--Davidson algorithm for interior
                 eigenvalues / 228 \\
                 12.6 Harmonic Ritz values and vectors / 229 \\
                 12.7 Refined Ritz vectors / 231 \\
                 12.8 The generalized Schur decomposition / 233 \\
                 12.9 JDQZ: Computing a partial $ Q Z $ decomposition /
                 233 \\
                 12.9.1 Restart / 235 \\
                 12.9.2 Deflation / 235 \\
                 12.9.3 Algorithm / 236 \\
                 12.10 Jacobi--Davidson for nonlinear eigenvalue
                 problems / 236 \\
                 Bibliography / 239 \\
                 13 Rayleigh quotient and trace minimization / 241 \\
                 13.1 Introduction / 241 \\
                 13.2 The method of steepest descent / 242 \\
                 13.3 The conjugate gradient algorithm / 243 \\
                 13.4 Locally optimal PCG (LOPCG) / 247 \\
                 13.5 The block Rayleigh quotient minimization algorithm
                 (BRQMIN) / 250 \\
                 13.6 The locally-optimal block preconditioned conjugate
                 gradient method (LOBPCG) / 250 \\
                 13.7 A numerical example / 251 \\
                 13.8 Trace minimization / 253 \\
                 Bibliography / 258",
}


bibtype: article
filename: pagerank.bib
label: Mehrabian:2016:SWR
author: Abbas Mehrabian and Nick Wormald
title: It's a Small World for Random Surfers
journal: j-ALGORITHMICA
volume: 76
number: 2
pages: 344--380
month: oct
monthnumber: 10
year: 2016
CODEN: ALGOEJ
DOI: https://doi.org/10.1007/s00453-015-0034-6
ISSN: 0178-4617 (print), 1432-0541 (electronic) OR 01784617 (print), 14320541 (electronic)
ISSNL: 0178-4617 OR 01784617
bibdate: Tue Sep 20 10:36:26 MDT 2016
bibtimestamp: 2016.09.20 10:36:26 MDT
URL: http://link.springer.com/article/10.1007/s00453-015-0034-6
fjournal: Algorithmica
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/453
rawauthor: Abbas Mehrabian and Nick Wormald
rawtitle: It's a Small World for Random Surfers
bibsource:
http://link.springer.com/journal/453/76/2; https://www.math.utah.edu/pub/tex/bib/algorithmica.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

keywords:
Height of random trees; Large deviations; PageRank-based selection model; Probabilistic analysis; Random-surfer; Small-world phenomenon; Webgraph model

entry:
@Article{Mehrabian:2016:SWR,
  author =       "Abbas Mehrabian and Nick Wormald",
  title =        "It's a Small World for Random Surfers",
  journal =      j-ALGORITHMICA,
  volume =       "76",
  number =       "2",
  pages =        "344--380",
  month =        oct,
  year =         "2016",
  CODEN =        "ALGOEJ",
  DOI =          "https://doi.org/10.1007/s00453-015-0034-6",
  ISSN =         "0178-4617 (print), 1432-0541 (electronic)",
  ISSN-L =       "0178-4617",
  bibdate =      "Tue Sep 20 10:36:26 MDT 2016",
  bibsource =    "http://link.springer.com/journal/453/76/2;
                 https://www.math.utah.edu/pub/tex/bib/algorithmica.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/article/10.1007/s00453-015-0034-6",
  acknowledgement = ack-nhfb,
  fjournal =     "Algorithmica",
  journal-URL =  "http://link.springer.com/journal/453",
  keywords =     "Height of random trees; Large deviations;
                 PageRank-based selection model; Probabilistic analysis;
                 Random-surfer; Small-world phenomenon; Webgraph model",
}


bibtype: article
filename: pagerank.bib
label: Wang:2016:HEI
author: Sibo Wang and Youze Tang and Xiaokui Xiao and Yin Yang and Zengxiang Li
title: HubPPR: effective indexing for approximate personalized pagerank
journal: j-PROC-VLDB-ENDOWMENT
volume: 10
number: 3
pages: 205--216
month: nov
monthnumber: 11
year: 2016
ISSN: 2150-8097 OR 21508097
bibdate: Thu Dec 1 09:02:03 MST 2016
bibtimestamp: 2016.12.01 09:02:03 MST
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/citation.cfm?id=J1174
rawauthor: Sibo Wang and Youze Tang and Xiaokui Xiao and Yin Yang and Zengxiang Li
rawtitle: {HubPPR}: effective indexing for approximate personalized pagerank
abstract:
Personalized PageRank (PPR) computation is a fundamental operation in web search, social networks, and graph analysis. Given a graph $G$, a source $s$, and a target $t$, the PPR query $ Pi (s, t)$ returns the probability that a random walk on $G$ starting from $s$ terminates at $t$. Unlike global PageRank which can be effectively pre-computed and materialized, the PPR result depends on both the source and the target, rendering results materialization infeasible for large graphs. Existing indexing techniques have rather limited effectiveness; in fact, the current state-of-the-art solution, BiPPR, answers individual PPR queries without pre-computation or indexing, and yet it outperforms all previous index-based solutions. Motivated by this, we propose HubPPR, an effective indexing scheme for PPR computation with controllable tradeoffs for accuracy, query time, and memory consumption. The main idea is to pre-compute and index auxiliary information for selected hub nodes that are often involved in PPR processing. Going one step further, we extend HubPPR to answer top-$k$ PPR queries, which returns the $k$ nodes with the highest PPR values with respect to a source $s$, among a given set $T$ of target nodes. Extensive experiments demonstrate that compared to the current best solution BiPPR, HubPPR achieves up to 10x and 220x speedup for PPR and top-$k$ PPR processing, respectively, with moderate memory consumption. Notably, with a single commodity server, HubPPR answers a top-$k$ PPR query in seconds on graphs with billions of edges, with high accuracy and strong result quality guarantees.

rawabstract:
Personalized PageRank (PPR) computation is a fundamental operation in web search, social networks, and graph analysis. Given a graph $G$, a source $s$, and a target $t$, the PPR query $ \Pi (s, t)$ returns the probability that a random walk on $G$ starting from $s$ terminates at $t$. Unlike global PageRank which can be effectively pre-computed and materialized, the PPR result depends on both the source and the target, rendering results materialization infeasible for large graphs. Existing indexing techniques have rather limited effectiveness; in fact, the current state-of-the-art solution, BiPPR, answers individual PPR queries without pre-computation or indexing, and yet it outperforms all previous index-based solutions. Motivated by this, we propose HubPPR, an effective indexing scheme for PPR computation with controllable tradeoffs for accuracy, query time, and memory consumption. The main idea is to pre-compute and index auxiliary information for selected hub nodes that are often involved in PPR processing. Going one step further, we extend HubPPR to answer top-$k$ PPR queries, which returns the $k$ nodes with the highest PPR values with respect to a source $s$, among a given set $T$ of target nodes. Extensive experiments demonstrate that compared to the current best solution BiPPR, HubPPR achieves up to 10x and 220x speedup for PPR and top-$k$ PPR processing, respectively, with moderate memory consumption. Notably, with a single commodity server, HubPPR answers a top-$k$ PPR query in seconds on graphs with billions of edges, with high accuracy and strong result quality guarantees.

entry:
@Article{Wang:2016:HEI,
  author =       "Sibo Wang and Youze Tang and Xiaokui Xiao and Yin Yang
                 and Zengxiang Li",
  title =        "{HubPPR}: effective indexing for approximate
                 personalized pagerank",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "10",
  number =       "3",
  pages =        "205--216",
  month =        nov,
  year =         "2016",
  CODEN =        "????",
  ISSN =         "2150-8097",
  bibdate =      "Thu Dec 1 09:02:03 MST 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  abstract =     "Personalized PageRank (PPR) computation is a
                 fundamental operation in web search, social networks,
                 and graph analysis. Given a graph $G$, a source $s$,
                 and a target $t$, the PPR query $ \Pi (s, t)$ returns
                 the probability that a random walk on $G$ starting from
                 $s$ terminates at $t$. Unlike global PageRank which can
                 be effectively pre-computed and materialized, the PPR
                 result depends on both the source and the target,
                 rendering results materialization infeasible for large
                 graphs. Existing indexing techniques have rather
                 limited effectiveness; in fact, the current
                 state-of-the-art solution, BiPPR, answers individual
                 PPR queries without pre-computation or indexing, and
                 yet it outperforms all previous index-based solutions.
                 Motivated by this, we propose HubPPR, an effective
                 indexing scheme for PPR computation with controllable
                 tradeoffs for accuracy, query time, and memory
                 consumption. The main idea is to pre-compute and index
                 auxiliary information for selected hub nodes that are
                 often involved in PPR processing. Going one step
                 further, we extend HubPPR to answer top-$k$ PPR
                 queries, which returns the $k$ nodes with the highest
                 PPR values with respect to a source $s$, among a given
                 set $T$ of target nodes. Extensive experiments
                 demonstrate that compared to the current best solution
                 BiPPR, HubPPR achieves up to 10x and 220x speedup for
                 PPR and top-$k$ PPR processing, respectively, with
                 moderate memory consumption. Notably, with a single
                 commodity server, HubPPR answers a top-$k$ PPR query in
                 seconds on graphs with billions of edges, with high
                 accuracy and strong result quality guarantees.",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "http://portal.acm.org/citation.cfm?id=J1174",
}


bibtype: article
filename: pagerank.bib
label: Zhang:2016:FAE
author: Hong-Fan Zhang and Ting-Zhu Huang and Chun Wen and Zhao-Li Shen
title: FOM accelerated by an extrapolation method for solving PageRank problems
journal: j-J-COMPUT-APPL-MATH
volume: 296
pages: 397--409
month: apr
monthnumber: 4
year: 2016
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Sat Feb 25 13:34:55 MST 2017
bibtimestamp: 2017.02.25 13:34:55 MST
URL: http://www.sciencedirect.com/science/article/pii/S0377042715004793
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Hong-Fan Zhang and Ting-Zhu Huang and Chun Wen and Zhao-Li Shen
rawtitle: {FOM} accelerated by an extrapolation method for solving {PageRank} problems
bibsource:
https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Zhang:2016:FAE,
  author =       "Hong-Fan Zhang and Ting-Zhu Huang and Chun Wen and
                 Zhao-Li Shen",
  title =        "{FOM} accelerated by an extrapolation method for
                 solving {PageRank} problems",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "296",
  number =       "??",
  pages =        "397--409",
  month =        apr,
  year =         "2016",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Sat Feb 25 13:34:55 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042715004793",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
}


