# bibtab

bibtype: inproceedings
filename: pagerank.bib
label: Kolda:2005:HOW
author: T. G. Kolda and B. W. Bader and J. P. Kenny
title: Higher-order Web link analysis using multilinear algebra
crossref: Han:2005:FII
pages: ??--??
year: 2005
DOI: https://doi.org/10.1109/ICDM.2005.77
bibdate: Thu May 06 15:45:35 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:45:35 ???
acknowledgement: ack-nhfb
rawauthor: T. G. Kolda and B. W. Bader and J. P. Kenny
rawtitle: Higher-order {Web} link analysis using multilinear algebra
abstract:
Linear algebra is a powerful and proven tool in Web search. Techniques, such as the PageRank algorithm of Brin and Page and the HITS algorithm of Kleinberg, score Web pages based on the principal eigenvector (or singular vector) of a particular non-negative matrix that captures the hyperlink structure of the Web graph. We propose and test a new methodology that uses multilinear algebra to elicit more information from a higher-order representation of the hyperlink graph. We start by labeling the edges in our graph with the anchor text of the hyperlinks so that the associated linear algebra representation is a sparse, three-way tensor. The first two dimensions of the tensor represent the Web pages while the third dimension adds the anchor text. We then use the rank-1 factors of a multilinear PARAFAC tensor decomposition, which are akin to singular vectors of the SVD, to automatically identify topics in the collection along with the associated authoritative Web pages.

rawabstract:
Linear algebra is a powerful and proven tool in Web search. Techniques, such as the PageRank algorithm of Brin and Page and the HITS algorithm of Kleinberg, score Web pages based on the principal eigenvector (or singular vector) of a particular non-negative matrix that captures the hyperlink structure of the Web graph. We propose and test a new methodology that uses multilinear algebra to elicit more information from a higher-order representation of the hyperlink graph. We start by labeling the edges in our graph with the anchor text of the hyperlinks so that the associated linear algebra representation is a sparse, three-way tensor. The first two dimensions of the tensor represent the Web pages while the third dimension adds the anchor text. We then use the rank-1 factors of a multilinear PARAFAC tensor decomposition, which are akin to singular vectors of the SVD, to automatically identify topics in the collection along with the associated authoritative Web pages.

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


bibtype: inproceedings
filename: pagerank.bib
label: Kurland:2005:PHS
author: Oren Kurland and Lillian Lee
editor: ACM
title: PageRank without hyperlinks: structural re-ranking using links induced by language models
publisher: pub-ACM
address: pub-ACM:adr
pages: 306--313
year: 2005
DOI: https://doi.org/10.1145/383952.384019
ISBN: 1-59593-034-5 OR 1595930345
ISBN13: 978-1-59593-034-7 OR 9781595930347
bibdate: Sat May 8 18:33:07 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:07 MDT
acknowledgement: ack-nhfb
rawauthor: Oren Kurland and Lillian Lee
raweditor: {ACM}
rawtitle: {PageRank} without hyperlinks: structural re-ranking using links induced by language models
booktitle:
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

abstract:
Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural re-ranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another; in doing so, we take care to prevent bias against long documents. We study a number of re-ranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks.

keywords:
authorities; graph-based retrieval; high-accuracy retrieval; HITS; hubs; language modeling; PageRank; social networks; structural re-ranking

rawabstract:
Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural re-ranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another; in doing so, we take care to prevent bias against long documents. We study a number of re-ranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks.

rawbooktitle:
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

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


bibtype: article
filename: pagerank.bib
label: Langville:2005:RPP
author: Amy N. Langville and Carl D. Meyer
title: A Reordering for the PageRank Problem
journal: j-SIAM-J-SCI-COMP
volume: 27
number: 6
pages: 2112--2120
month: nov
monthnumber: 11
year: 2005
CODEN: SJOCE3
DOI: https://doi.org/10.1137/040607551
ISSN: 1064-8275 (print), 1095-7197 (electronic) OR 10648275 (print), 10957197 (electronic)
ISSNL: 1064-8275 OR 10648275
MRclass: 68U35 (65F30); 65F30 65C40 60J22 65F50
MRnumber: MR2211442 (2006k:68167)
bibdate: Tue Jun 27 09:24:29 MDT 2006
bibsource: http://epubs.siam.org/sam-bin/dbq/toc/SISC/27/6; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2006.06.27 09:24:29 MDT
fjournal: SIAM Journal on Scientific Computing
ZMnumber: 1103.65048
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sisc
rawauthor: Amy N. Langville and Carl D. Meyer
rawtitle: A Reordering for the {PageRank} Problem
URL:
http://epubs.siam.org/volume-27/art_60755.html; https://www.math.utah.edu/pub/tex/bib/siamjscicomput.bib

abstract:
We describe a reordering particularly suited to the PageRank problem, which reduces the computation of the PageRank vector to that of solving a much smaller system and then using forward substitution to get the full solution vector. We compare the theoretical rates of convergence of the original PageRank algorithm to that of the new reordered PageRank algorithm, showing that the new algorithm can do no worse than the original algorithm. We present results of an experimental comparison on five datasets, which demonstrate that the reordered PageRank algorithm can provide a speedup of as much as a factor of 6. We also note potential additional benefits that result from the proposed reordering.

rawabstract:
We describe a reordering particularly suited to the PageRank problem, which reduces the computation of the PageRank vector to that of solving a much smaller system and then using forward substitution to get the full solution vector. We compare the theoretical rates of convergence of the original PageRank algorithm to that of the new reordered PageRank algorithm, showing that the new algorithm can do no worse than the original algorithm. We present results of an experimental comparison on five datasets, which demonstrate that the reordered PageRank algorithm can provide a speedup of as much as a factor of 6. We also note potential additional benefits that result from the proposed reordering.

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


bibtype: article
filename: pagerank.bib
label: Langville:2005:UMC
author: Amy N. Langville and Carl D. Meyer
title: Updating Markov Chains with an Eye on Google's PageRank
journal: j-SIAM-J-MAT-ANA-APPL
volume: 27
number: 4
pages: 968--987
year: 2005
CODEN: SJMAEL
DOI: https://doi.org/10.1137/040619028
ISSN: 0895-4798 (print), 1095-7162 (electronic) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
fjournal: SIAM Journal on Matrix Analysis and Applications
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
rawauthor: Amy N. Langville and Carl D. Meyer
rawtitle: Updating {Markov} Chains with an Eye on {Google}'s {PageRank}
abstract:
An iterative algorithm based on aggregation/disaggregation principles is presented for updating the stationary distribution of a finite homogeneous irreducible Markov chain. The focus is on large-scale problems of the kind that are characterized by Google's PageRank application, but the algorithm is shown to work well in general contexts. The algorithm is flexible in that it allows for changes to the transition probabilities as well as for the creation or deletion of states. In addition to establishing the rate of convergence, it is proven that the algorithm is globally convergent. Results of numerical experiments are presented.

keywords:
aggregation/disaggregation; Google; Markov chains; PageRank; stationary vector; stochastic complementation; updating

rawabstract:
An iterative algorithm based on aggregation/disaggregation principles is presented for updating the stationary distribution of a finite homogeneous irreducible Markov chain. The focus is on large-scale problems of the kind that are characterized by Google's PageRank application, but the algorithm is shown to work well in general contexts. The algorithm is flexible in that it allows for changes to the transition probabilities as well as for the creation or deletion of states. In addition to establishing the rate of convergence, it is proven that the algorithm is globally convergent. Results of numerical experiments are presented.

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


bibtype: inproceedings
filename: pagerank.bib
label: Liu:2005:WIA
author: Tie-Yan Liu and Wei-Ying Ma
title: Webpage importance analysis using conditional Markov random walk
crossref: Skowron:2005:PIW
pages: 515--521
year: 2005
DOI: https://doi.org/10.1109/WI.2005.161
bibdate: Thu May 06 16:39:05 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:39:05 ???
acknowledgement: ack-nhfb
rawauthor: Tie-Yan Liu and Wei-Ying Ma
rawtitle: Webpage importance analysis using conditional {Markov} random walk
entry:
@InProceedings{Liu:2005:WIA,
  author =       "Tie-Yan Liu and Wei-Ying Ma",
  title =        "Webpage importance analysis using conditional {Markov}
                 random walk",
  crossref =     "Skowron:2005:PIW",
  pages =        "515--521",
  year =         "2005",
  DOI =          "https://doi.org/10.1109/WI.2005.161",
  bibdate =      "Thu May 06 16:39:05 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


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

rawabstract:
PageRank becomes the most well-known re-ranking technique of the search results. By its iterative computational nature, the computation takes much computing time and resource. Researchers have then devoted much attention in studying an efficient way to compute the PageRank scores of a very large web graph. However, only a few of them focus on large-scale PageRank computation using parallel processing techniques. In this paper, we propose a Partition-based parallel PageRank algorithm that can efficiently run on a low-cost parallel environment like the PC cluster. For comparison, we also study the other two known techniques, as well as propose an analytical discussion concerning I/O and synchronization cost, and memory usage. Experimental results with two web graphs synthesized from the {\tt .TH} domain and the Stanford WebBase project are very promising.

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


bibtype: inproceedings
filename: pagerank.bib
label: Martins:2005:GRA
author: B. Martins and M. J. Silva
title: A graph-ranking algorithm for geo-referencing documents
crossref: Han:2005:FII
pages: ??--??
year: 2005
DOI: https://doi.org/10.1109/ICDM.2005.6
bibdate: Thu May 06 15:33:58 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:33:58 ???
acknowledgement: ack-nhfb
rawauthor: B. Martins and M. J. Silva
rawtitle: A graph-ranking algorithm for geo-referencing documents
entry:
@InProceedings{Martins:2005:GRA,
  author =       "B. Martins and M. J. Silva",
  title =        "A graph-ranking algorithm for geo-referencing
                 documents",
  crossref =     "Han:2005:FII",
  pages =        "??--??",
  year =         "2005",
  DOI =          "https://doi.org/10.1109/ICDM.2005.6",
  bibdate =      "Thu May 06 15:33:58 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  pagecount =    "4",
}


bibtype: phdthesis
filename: pagerank.bib
label: Mason:2005:DCP
author: Kahn Mason
title: Detecting colluders in PageRank finding slow mixing states in a Markov chain
type: Thesis (Ph.D.)
school: Stanford University
address: Stanford, CA, USA
pages: 75
year: 2005
ISBN: 0-542-29567-9 OR 0542295679
ISBN13: 978-0-542-29567-6 OR 9780542295676
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
note: Order number AAI3187317.
URL: http://wwwlib.umi.com/dissertations/fullcit/3187317
acknowledgement: ack-nhfb
advisor: Benjamin Van Roy
rawauthor: Kahn Mason
rawnote: Order number AAI3187317.
rawtitle: Detecting colluders in {PageRank} finding slow mixing states in a {Markov} chain
abstract:
The PageRank algorithm evaluates webpage reputations based on the hyperlinks that connect them. Webpages that collude to boost their reputations significantly distort the resulting rankings. We introduce a measure for assessing the degree to which a set of webpages boosts its reputation. There is no known efficient algorithm that is guaranteed to detect significantly boosted sets when they exist. However, we provide metrics that, under reasonable conditions, are guaranteed to detect a member of a significantly boosted set, if one exists, and address various implementation issues that arise in incorporating these metrics into PageRank.

rawabstract:
The PageRank algorithm evaluates webpage reputations based on the hyperlinks that connect them. Webpages that collude to boost their reputations significantly distort the resulting rankings. We introduce a measure for assessing the degree to which a set of webpages boosts its reputation. There is no known efficient algorithm that is guaranteed to detect significantly boosted sets when they exist. However, we provide metrics that, under reasonable conditions, are guaranteed to detect a member of a significantly boosted set, if one exists, and address various implementation issues that arise in incorporating these metrics into PageRank.

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


bibtype: inproceedings
filename: pagerank.bib
label: Massa:2005:PRU
author: P. Massa and C. Hayes
title: Page-reRank: using trusted links to re-rank authority
crossref: Skowron:2005:PIW
pages: 614--617
year: 2005
DOI: https://doi.org/10.1109/WI.2005.112
bibdate: Thu May 06 16:22:59 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:22:59 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: P. Massa and C. Hayes
rawtitle: {Page-reRank}: using trusted links to re-rank authority
entry:
@InProceedings{Massa:2005:PRU,
  author =       "P. Massa and C. Hayes",
  title =        "{Page-reRank}: using trusted links to re-rank
                 authority",
  crossref =     "Skowron:2005:PIW",
  pages =        "614--617",
  year =         "2005",
  DOI =          "https://doi.org/10.1109/WI.2005.112",
  bibdate =      "Thu May 06 16:22:59 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: McSherry:2005:UAA
author: Frank McSherry
editor: ACM
title: A uniform approach to accelerated PageRank computation
publisher: pub-ACM
address: pub-ACM:adr
pages: 575--582
year: 2005
DOI: https://doi.org/10.1145/775152.775191
ISBN: 1-59593-046-9 OR 1595930469
ISBN13: 978-1-59593-046-0 OR 9781595930460
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
keywords: link analysis; PageRank; random walks; web graph
acknowledgement: ack-nhfb
rawauthor: Frank McSherry
raweditor: {ACM}
rawtitle: A uniform approach to accelerated {PageRank} computation
booktitle:
International World Wide Web Conference Proceedings of the 14th international conference on World Wide Web

abstract:
In this note we consider a simple reformulation of the traditional power iteration algorithm for computing the stationary distribution of a Markov chain. Rather than communicate their current probability values to their neighbors at each step, nodes instead communicate only changes in probability value. This reformulation enables a large degree of flexibility in the manner in which nodes update their values, leading to an array of optimizations and features, including faster convergence, efficient incremental updating, and a robust distributed implementation.While the spirit of many of these optimizations appear in previous literature, we observe several cases where this unification simplifies previous work, removing technical complications and extending their range of applicability. We implement and measure the performance of several optimizations on a sizable (34M node) web subgraph, seeing significant composite performance gains, especially for the case of incremental recomputation after changes to the web graph.

rawabstract:
In this note we consider a simple reformulation of the traditional power iteration algorithm for computing the stationary distribution of a Markov chain. Rather than communicate their current probability values to their neighbors at each step, nodes instead communicate only changes in probability value. This reformulation enables a large degree of flexibility in the manner in which nodes update their values, leading to an array of optimizations and features, including faster convergence, efficient incremental updating, and a robust distributed implementation.While the spirit of many of these optimizations appear in previous literature, we observe several cases where this unification simplifies previous work, removing technical complications and extending their range of applicability. We implement and measure the performance of several optimizations on a sizable (34M node) web subgraph, seeing significant composite performance gains, especially for the case of incremental recomputation after changes to the web graph.

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


bibtype: article
filename: pagerank.bib
label: Morrison:2005:GUS
author: Julie L. Morrison and Rainer Breitling and Desmond J. Higham and David R. Gilbert
title: GeneRank: Using search engine technology for the analysis of microarray experiments
journal: j-BMC-BIOINFORMATICS
volume: 6
pages: 233--239
year: 2005
CODEN: BBMIC4
DOI: https://doi.org/10.1186/1471-2105-6-233
ISSN: 1471-2105 OR 14712105
ISSNL: 1471-2105 OR 14712105
bibdate: Tue Aug 11 17:28:42 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:28:42 ???
URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1261158/
fjournal: BMC Bioinformatics
acknowledgement: ack-nhfb
journalURL: http://www.biomedcentral.com/bmcbioinformatics/
rawauthor: Julie L. Morrison and Rainer Breitling and Desmond J. Higham and David R. Gilbert
rawtitle: {GeneRank}: Using search engine technology for the analysis of microarray experiments
entry:
@Article{Morrison:2005:GUS,
  author =       "Julie L. Morrison and Rainer Breitling and Desmond J.
                 Higham and David R. Gilbert",
  title =        "{GeneRank}: Using search engine technology for the
                 analysis of microarray experiments",
  journal =      j-BMC-BIOINFORMATICS,
  volume =       "6",
  number =       "??",
  pages =        "233--239",
  month =        "??",
  year =         "2005",
  CODEN =        "BBMIC4",
  DOI =          "https://doi.org/10.1186/1471-2105-6-233",
  ISSN =         "1471-2105",
  ISSN-L =       "1471-2105",
  bibdate =      "Tue Aug 11 17:28:42 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1261158/",
  acknowledgement = ack-nhfb,
  fjournal =     "BMC Bioinformatics",
  journal-URL =  "http://www.biomedcentral.com/bmcbioinformatics/",
}


bibtype: inproceedings
filename: pagerank.bib
label: Padmanabhan:2005:WWI
author: D. Padmanabhan and P. Desikan and J. Srivastava and K. Riaz
title: WICER: a weighted inter-cluster edge ranking for clustered graphs
crossref: Skowron:2005:PIW
pages: 522--528
year: 2005
DOI: https://doi.org/10.1109/WI.2005.166
bibdate: Thu May 06 16:37:10 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:37:10 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: D. Padmanabhan and P. Desikan and J. Srivastava and K. Riaz
rawtitle: {WICER}: a weighted inter-cluster edge ranking for clustered graphs
entry:
@InProceedings{Padmanabhan:2005:WWI,
  author =       "D. Padmanabhan and P. Desikan and J. Srivastava and K.
                 Riaz",
  title =        "{WICER}: a weighted inter-cluster edge ranking for
                 clustered graphs",
  crossref =     "Skowron:2005:PIW",
  pages =        "522--528",
  year =         "2005",
  DOI =          "https://doi.org/10.1109/WI.2005.166",
  bibdate =      "Thu May 06 16:37:10 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


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

rawabstract:
A re-ranking technique, called 'PageRank' brings a successful story behind the Google search engine. Many studies focus on finding an efficient way to compute the PageRank scores of a large web graph. Researchers propose to compute them sequentially by reducing the I/O cost of disk access, improving the convergence rate, or even employing Peer-2-Peer architecture, etc. However, only a few concentrate on computation using parallel processing techniques. In this paper, we propose a Partition-based parallel PageRank algorithm that can be efficiently run on a low-cost parallel environment like PC cluster. For comparison, we also study other two well-known PageRank techniques, and provide an analytical discussion of their performance in terms of I/O and synchronization cost, as well as memory usage. Experimental results show a promising improvement on a large artificial web graph synthesized from the {\tt .TH} domain.

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


bibtype: article
filename: pagerank.bib
label: Serra-Capizzano:2005:JCF
author: Stefano Serra-Capizzano
title: Jordan canonical form of the Google matrix: a potential contribution to the PageRank computation
journal: j-SIAM-J-MAT-ANA-APPL
volume: 27
number: 2
pages: 305--312
month: apr
monthnumber: 4
year: 2005
CODEN: SJMAEL
DOI: https://doi.org/10.1137/S0895479804441407
ISSN: 0895-4798 (print), 1095-7162 (electronic) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
MRclass: 15A18 (15A21)
MRnumber: MR2179674 (2006g:15019)
bibdate: Thu Dec 29 16:33:54 MST 2005
bibtimestamp: 2005.12.29 16:33:54 MST
note: See comments cite Wu:2008:CJC.
fjournal: SIAM Journal on Matrix Analysis and Applications
ZMnumber: 1103.65051
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
rawauthor: Stefano Serra-Capizzano
rawnote: See comments \cite{Wu:2008:CJC}.
bibsource:
http://epubs.siam.org/sam-bin/dbq/toc/SIMAX/27/2; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

URL:
http://epubs.siam.org/sam-bin/dbq/article/44140; https://www.math.utah.edu/pub/tex/bib/siamjmatanaappl.bib

rawtitle:
{Jordan} canonical form of the {Google} matrix: a potential contribution to the {PageRank} computation

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


bibtype: inproceedings
filename: pagerank.bib
label: Tarau:2005:SDE
author: Paul Tarau and Rada Mihalcea and Elizabeth Figa
editor: ACM
booktitle: Proceedings of the 2005 ACM Symposium on Applied computing
title: Semantic document engineering with WordNet and PageRank
publisher: pub-ACM
address: pub-ACM:adr
pages: 782--786
year: 2005
DOI: https://doi.org/10.3115/981658.981684
ISBN: 1-58113-964-0 OR 1581139640
ISBN13: 978-1-58113-964-8 OR 9781581139648
bibdate: Sat May 8 18:33:04 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:04 MDT
acknowledgement: ack-nhfb
rawauthor: Paul Tarau and Rada Mihalcea and Elizabeth Figa
rawbooktitle: Proceedings of the 2005 ACM Symposium on Applied computing
raweditor: {ACM}
rawtitle: Semantic document engineering with {WordNet} and {PageRank}
abstract:
This paper describes Natural Language Processing techniques for document engineering in combination with graph algorithms and statistical methods. Google's PageRank and similar fast-converging recursive graph algorithms have provided practical means to statically rank vertices of large graphs like the World Wide Web. By combining a fast Java-based PageRank implementation with a Prolog base inferential layer, running on top of an optimized WordNet graph, we describe applications to word sense disambiguation and evaluate their accuracy on standard benchmarks.

keywords:
logic programming; natural language processing; PageRank-style graph algorithms; semantics-based document processing; word sense disambiguation; WordNet

rawabstract:
This paper describes Natural Language Processing techniques for document engineering in combination with graph algorithms and statistical methods. Google's PageRank and similar fast-converging recursive graph algorithms have provided practical means to statically rank vertices of large graphs like the World Wide Web. By combining a fast Java-based PageRank implementation with a Prolog base inferential layer, running on top of an optimized WordNet graph, we describe applications to word sense disambiguation and evaluate their accuracy on standard benchmarks.

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


bibtype: inproceedings
filename: pagerank.bib
label: Tummarello:2005:SAH
author: G. Tummarello and C. Morbidoni and P. Puliti and F. Piazza
title: Semantic audio hyperlinking: a multimedia-semantic Web scenario
crossref: Nesi:2005:FIC
pages: ??--??
year: 2005
DOI: https://doi.org/10.1109/AXMEDIS.2005.45
bibdate: Thu May 06 15:54:55 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:54:55 ???
acknowledgement: ack-nhfb
rawauthor: G. Tummarello and C. Morbidoni and P. Puliti and F. Piazza
rawtitle: Semantic audio hyperlinking: a multimedia-semantic {Web} scenario
entry:
@InProceedings{Tummarello:2005:SAH,
  author =       "G. Tummarello and C. Morbidoni and P. Puliti and F.
                 Piazza",
  title =        "Semantic audio hyperlinking: a multimedia-semantic
                 {Web} scenario",
  crossref =     "Nesi:2005:FIC",
  pages =        "??--??",
  year =         "2005",
  DOI =          "https://doi.org/10.1109/AXMEDIS.2005.45",
  bibdate =      "Thu May 06 15:54:55 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Vigna:2005:TTP
author: Sebastiano Vigna
editor: Tatsuya Hagino and Allan Ellis
title: TruRank: Taking PageRank to the limit
publisher: pub-ACM
address: pub-ACM:adr
pages: 976--977
year: 2005
DOI: https://doi.org/10.1145/1062745.1062826
ISBN: 1-59593-051-5 OR 1595930515
ISBN13: 978-1-59593-051-4 OR 9781595930514
LCCN: TK5105.888 I573 2005
bibdate: Tue Aug 11 17:39:04 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:39:04 ???
acknowledgement: ack-nhfb
bookpages: 1192
rawauthor: Sebastiano Vigna
raweditor: Tatsuya Hagino and Allan Ellis
rawtitle: {TruRank}: Taking {PageRank} to the limit
booktitle:
Special Interest Tracks and Posters of the 14th International Conference on the World Wide Web, WWW 05. Chiba, Japan, May 10--14, 2005

rawbooktitle:
{Special Interest Tracks and Posters of the 14th International Conference on the World Wide Web, WWW 05. Chiba, Japan, May 10--14, 2005}

entry:
@InProceedings{Vigna:2005:TTP,
  author =       "Sebastiano Vigna",
  editor =       "Tatsuya Hagino and Allan Ellis",
  booktitle =    "{Special Interest Tracks and Posters of the 14th
                 International Conference on the World Wide Web, WWW 05.
                 Chiba, Japan, May 10--14, 2005}",
  title =        "{TruRank}: Taking {PageRank} to the limit",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "976--977",
  year =         "2005",
  DOI =          "https://doi.org/10.1145/1062745.1062826",
  ISBN =         "1-59593-051-5",
  ISBN-13 =      "978-1-59593-051-4",
  LCCN =         "TK5105.888 I573 2005",
  bibdate =      "Tue Aug 11 17:39:04 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  bookpages =    "1192",
}


bibtype: inproceedings
filename: pagerank.bib
label: Wang:2005:DP
author: Xuanhui Wang and Azadeh Shakery and Tao Tao
editor: ACM
title: Dirichlet PageRank
publisher: pub-ACM
address: pub-ACM:adr
pages: 661--662
year: 2005
DOI: https://doi.org/10.1145/383952.384019
ISBN: 1-59593-034-5 OR 1595930345
ISBN13: 978-1-59593-034-7 OR 9781595930347
bibdate: Sat May 8 18:33:11 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:11 MDT
acknowledgement: ack-nhfb
rawauthor: Xuanhui Wang and Azadeh Shakery and Tao Tao
raweditor: {ACM}
rawtitle: {Dirichlet PageRank}
booktitle:
Annual ACM Conference on Research and Development in Information Retrieval Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

abstract:
PageRank has been known to be a successful algorithm in ranking web sources. In order to avoid the rank sink problem, PageRank assumes that a surfer, being in a page, jumps to a random page with a certain probability. In the standard PageRank algorithm, the jumping probabilities are assumed to be the same for all the pages, regardless of the page properties. This is not the case in the real world, since presumably a surfer would more likely follow the out-links of a high-quality hub page than follow the links of a low-quality one. In this poster, we propose a novel algorithm `Dirichlet PageRank' to address this problem by adapting flexible jumping probabilities based on the number of out-links in a page. Empirical results on TREC data show that our method outperforms the standard PageRank algorithm.

rawabstract:
PageRank has been known to be a successful algorithm in ranking web sources. In order to avoid the rank sink problem, PageRank assumes that a surfer, being in a page, jumps to a random page with a certain probability. In the standard PageRank algorithm, the jumping probabilities are assumed to be the same for all the pages, regardless of the page properties. This is not the case in the real world, since presumably a surfer would more likely follow the out-links of a high-quality hub page than follow the links of a low-quality one. In this poster, we propose a novel algorithm `Dirichlet PageRank' to address this problem by adapting flexible jumping probabilities based on the number of out-links in a page. Empirical results on TREC data show that our method outperforms the standard PageRank algorithm.

rawbooktitle:
Annual ACM Conference on Research and Development in Information Retrieval Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

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


bibtype: article
filename: pagerank.bib
label: Weingart:2005:IBU
author: Peter Weingart
title: Impact of bibliometrics upon the science system: Inadvertent consequences?
journal: j-SCIENTOMETRICS
volume: 62
number: 1
pages: 117--131
month: jan
monthnumber: 1
year: 2005
CODEN: SCNTDX
DOI: https://doi.org/10.1007/s11192-005-0007-7
ISSN: 0138-9130 (print), 1588-2861 (electronic) OR 01389130 (print), 15882861 (electronic)
ISSNL: 0138-9130 OR 01389130
bibdate: Thu Jun 02 08:35:18 2011
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.02 08:35:18 ???
URL: http://link.springer.com/article/10.1007/s11192-005-0007-7
fjournal: Scientometrics
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11192
rawauthor: Peter Weingart
rawtitle: Impact of bibliometrics upon the science system: Inadvertent consequences?
abstract:
Ranking of research institutions by bibliometric methods is an improper tool for research performance evaluation, even at the level of large institutions. The problem, however, is not the ranking as such. The indicators used for ranking are often not advanced enough, and this situation is part of the broader problem of the application of insufficiently developed bibliometric indicators used by persons who do not have clear competence and experience in the field of quantitative studies of science. After a brief overview of the basic elements of bibliometric analysis, I discuss the major technical and methodological problems in the application of publication and citation data in the context of evaluation. Then I contend that the core of the problem lies not necessarily at the side of the data producer. Quite often persons responsible for research performance evaluation, for instance scientists themselves in their role as head of institutions and departments, science administrators at the government level and other policy makers show an attitude that encourages `quick and dirty' bibliometric analyses whereas better quality is available. Finally, the necessary conditions for a successful application of advanced bibliometric indicators as support tool for peer review are discussed.

rawabstract:
Ranking of research institutions by bibliometric methods is an improper tool for research performance evaluation, even at the level of large institutions. The problem, however, is not the ranking as such. The indicators used for ranking are often not advanced enough, and this situation is part of the broader problem of the application of insufficiently developed bibliometric indicators used by persons who do not have clear competence and experience in the field of quantitative studies of science. After a brief overview of the basic elements of bibliometric analysis, I discuss the major technical and methodological problems in the application of publication and citation data in the context of evaluation. Then I contend that the core of the problem lies not necessarily at the side of the data producer. Quite often persons responsible for research performance evaluation, for instance scientists themselves in their role as head of institutions and departments, science administrators at the government level and other policy makers show an attitude that encourages `quick and dirty' bibliometric analyses whereas better quality is available. Finally, the necessary conditions for a successful application of advanced bibliometric indicators as support tool for peer review are discussed.

entry:
@Article{Weingart:2005:IBU,
  author =       "Peter Weingart",
  title =        "Impact of bibliometrics upon the science system:
                 Inadvertent consequences?",
  journal =      j-SCIENTOMETRICS,
  volume =       "62",
  number =       "1",
  pages =        "117--131",
  month =        jan,
  year =         "2005",
  CODEN =        "SCNTDX",
  DOI =          "https://doi.org/10.1007/s11192-005-0007-7",
  ISSN =         "0138-9130 (print), 1588-2861 (electronic)",
  ISSN-L =       "0138-9130",
  bibdate =      "Thu Jun 02 08:35:18 2011",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/article/10.1007/s11192-005-0007-7",
  abstract =     "Ranking of research institutions by bibliometric
                 methods is an improper tool for research performance
                 evaluation, even at the level of large institutions.
                 The problem, however, is not the ranking as such. The
                 indicators used for ranking are often not advanced
                 enough, and this situation is part of the broader
                 problem of the application of insufficiently developed
                 bibliometric indicators used by persons who do not have
                 clear competence and experience in the field of
                 quantitative studies of science. After a brief overview
                 of the basic elements of bibliometric analysis, I
                 discuss the major technical and methodological problems
                 in the application of publication and citation data in
                 the context of evaluation. Then I contend that the core
                 of the problem lies not necessarily at the side of the
                 data producer. Quite often persons responsible for
                 research performance evaluation, for instance
                 scientists themselves in their role as head of
                 institutions and departments, science administrators at
                 the government level and other policy makers show an
                 attitude that encourages `quick and dirty' bibliometric
                 analyses whereas better quality is available. Finally,
                 the necessary conditions for a successful application
                 of advanced bibliometric indicators as support tool for
                 peer review are discussed.",
  acknowledgement = ack-nhfb,
  fjournal =     "Scientometrics",
  journal-URL =  "http://link.springer.com/journal/11192",
}


bibtype: inproceedings
filename: pagerank.bib
label: Wu:2005:ULM
author: Jie Wu and K. Aberer
title: Using a Layered Markov Model for Distributed Web Ranking Computation
crossref: IEEE:2005:ICD
pages: 533--542
year: 2005
DOI: https://doi.org/10.1109/ICDCS.2005.84
bibdate: Thu May 06 15:11:10 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:11:10 ???
acknowledgement: ack-nhfb
rawauthor: Jie Wu and K. Aberer
rawtitle: Using a Layered {Markov} Model for Distributed {Web} Ranking Computation
entry:
@InProceedings{Wu:2005:ULM,
  author =       "Jie Wu and K. Aberer",
  title =        "Using a Layered {Markov} Model for Distributed {Web}
                 Ranking Computation",
  crossref =     "IEEE:2005:ICD",
  pages =        "533--542",
  year =         "2005",
  DOI =          "https://doi.org/10.1109/ICDCS.2005.84",
  bibdate =      "Thu May 06 15:11:10 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Yang:2005:RMW
author: Christopher C. Yang and K. Y. Chan
editor: ACM
title: Retrieving multimedia Web objects based on PageRank algorithm
publisher: pub-ACM
address: pub-ACM:adr
pages: 906--907
year: 2005
DOI: https://doi.org/10.1145/511446.511454
ISBN: 1-59593-051-5 OR 1595930515
ISBN13: 978-1-59593-051-4 OR 9781595930514
bibdate: Sat May 8 18:33:11 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:11 MDT
acknowledgement: ack-nhfb
rawauthor: Christopher C. Yang and K. Y. Chan
raweditor: {ACM}
rawtitle: Retrieving multimedia {Web} objects based on {PageRank} algorithm
booktitle:
International World Wide Web Conference Special interest tracks and posters of the 14th international conference on World Wide Web

abstract:
Hyperlink analysis has been widely investigated to support the retrieval of Web documents in Internet search engines. It has been proven that the hyperlink analysis significantly improves the relevance of the search results and these techniques have been adopted in many commercial search engines, e.g. Google. However, hyperlink analysis is mostly utilized in the ranking mechanism of Web pages only but not including other multimedia objects, such as images and video. In this project, we propose a modified Multimedia PageRank algorithm to support the searching of multimedia objects in the Web.

keywords:
content based retrieval; HITS; hyperlink analysis; multimedia retrieval; PageRank; web search engines

rawabstract:
Hyperlink analysis has been widely investigated to support the retrieval of Web documents in Internet search engines. It has been proven that the hyperlink analysis significantly improves the relevance of the search results and these techniques have been adopted in many commercial search engines, e.g. Google. However, hyperlink analysis is mostly utilized in the ranking mechanism of Web pages only but not including other multimedia objects, such as images and video. In this project, we propose a modified Multimedia PageRank algorithm to support the searching of multimedia objects in the Web.

rawbooktitle:
International World Wide Web Conference Special interest tracks and posters of the 14th international conference on World Wide Web

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


bibtype: inproceedings
filename: pagerank.bib
label: Yu:2005:ATD
author: P. S. Yu and Xin Li and Bing Liu
title: Adding the temporal dimension to search --- a case study in publication search
crossref: Skowron:2005:PIW
pages: 543--549
year: 2005
DOI: https://doi.org/10.1109/WI.2005.21
bibdate: Thu May 06 16:18:03 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:18:03 ???
acknowledgement: ack-nhfb
rawauthor: P. S. Yu and Xin Li and Bing Liu
rawtitle: Adding the temporal dimension to search --- a case study in publication search
entry:
@InProceedings{Yu:2005:ATD,
  author =       "P. S. Yu and Xin Li and Bing Liu",
  title =        "Adding the temporal dimension to search --- a case
                 study in publication search",
  crossref =     "Skowron:2005:PIW",
  pages =        "543--549",
  year =         "2005",
  DOI =          "https://doi.org/10.1109/WI.2005.21",
  bibdate =      "Thu May 06 16:18:03 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Zhang:2005:CBH
author: Junlin Zhang and Le Sun and Quan Zhou
title: A cue-based hub-authority approach for multi-document text summarization
crossref: IEEE:2005:PII
pages: 642--645
year: 2005
DOI: https://doi.org/10.1109/NLPKE.2005.1598815
bibdate: Thu May 06 15:31:40 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:31:40 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: Junlin Zhang and Le Sun and Quan Zhou
rawtitle: A cue-based hub-authority approach for multi-document text summarization
entry:
@InProceedings{Zhang:2005:CBH,
  author =       "Junlin Zhang and Le Sun and Quan Zhou",
  title =        "A cue-based hub-authority approach for multi-document
                 text summarization",
  crossref =     "IEEE:2005:PII",
  pages =        "642--645",
  year =         "2005",
  DOI =          "https://doi.org/10.1109/NLPKE.2005.1598815",
  bibdate =      "Thu May 06 15:31:40 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: Zhu:2005:DPC
author: Yangbo Zhu and Shaozhi Ye and Xing Li
editor: ACM
title: Distributed PageRank computation based on iterative aggregation-disaggregation methods
publisher: pub-ACM
address: pub-ACM:adr
pages: 578--585
year: 2005
DOI: https://doi.org/10.1145/1099554.1099705
ISBN: 1-59593-140-6 OR 1595931406
ISBN13: 978-1-59593-140-5 OR 9781595931405
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
keywords: block Jacobi; distributed search engines; iterative aggregation-disaggregation; PageRank
acknowledgement: ack-nhfb
rawauthor: Yangbo Zhu and Shaozhi Ye and Xing Li
raweditor: {ACM}
rawtitle: Distributed {PageRank} computation based on iterative aggregation-disaggregation methods
booktitle:
Conference on Information and Knowledge Management Proceedings of the 14th ACM international conference on Information and knowledge management

abstract:
PageRank has been widely used as a major factor in search engine ranking systems. However, global link graph information is required when computing PageRank, which causes prohibitive communication cost to achieve accurate results in distributed solution. In this paper, we propose a distributed PageRank computation algorithm based on iterative aggregation-disaggregation (IAD) method with Block Jacobi smoothing. The basic idea is divide-and-conquer. We treat each web site as a node to explore the block structure of hyperlinks. Local PageRank is computed by each node itself and then updated with a low communication cost with a coordinator. We prove the global convergence of the Block Jacobi method and then analyze the communication overhead and major advantages of our algorithm. Experiments on three real web graphs show that our method converges 5-7 times faster than the traditional Power method. We believe our work provides an efficient and practical distributed solution for PageRank on large scale Web graphs.

rawabstract:
PageRank has been widely used as a major factor in search engine ranking systems. However, global link graph information is required when computing PageRank, which causes prohibitive communication cost to achieve accurate results in distributed solution. In this paper, we propose a distributed PageRank computation algorithm based on iterative aggregation-disaggregation (IAD) method with Block Jacobi smoothing. The basic idea is divide-and-conquer. We treat each web site as a node to explore the block structure of hyperlinks. Local PageRank is computed by each node itself and then updated with a low communication cost with a coordinator. We prove the global convergence of the Block Jacobi method and then analyze the communication overhead and major advantages of our algorithm. Experiments on three real web graphs show that our method converges 5-7 times faster than the traditional Power method. We believe our work provides an efficient and practical distributed solution for PageRank on large scale Web graphs.

rawbooktitle:
Conference on Information and Knowledge Management Proceedings of the 14th ACM international conference on Information and knowledge management

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


bibtype: inproceedings
filename: pagerank.bib
label: Akian:2006:PMS
author: Marianne Akian and Stephane Gaubert and Laure Ninove
editor: Christian Commault and Nicolas Marchand
title: The $T$-PageRank: a model of self-validating effects of web surfing
volume: 341
publisher: pub-SV
address: pub-SV:adr
pages: 239--246
year: 2006
DOI: https://doi.org/10.1007/3-540-34774-7_31
ISBN: 3-540-34774-7, 3-540-34771-2 OR 3540347747, 3540347712
ISBN13: 978-3-540-34774-3, 978-3-540-34771-2 OR 9783540347743, 9783540347712
LCCN: QA402 .M86 2006
MRclass: 68U35
MRnumber: MR2250261
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
series: ser-LNCIS
ZMnumber: 1121.68007
acknowledgement: ack-nhfb
bookpages: xiv + 448
rawauthor: Marianne Akian and St{\'e}phane Gaubert and Laure Ninove
raweditor: Christian Commault and Nicolas Marchand
rawtitle: The {$T$-PageRank}: a model of self-validating effects of web surfing
booktitle:
Positive systems: proceedings of the second Multidisciplinary International Symposium on Positive Systems: Theory and Applications (POSTA 06), Grenoble, France, Aug. 30-31, Sept. 1, 2006

rawbooktitle:
{Positive systems: proceedings of the second Multidisciplinary International Symposium on Positive Systems: Theory and Applications (POSTA 06), Grenoble, France, Aug. 30-31, Sept. 1, 2006}

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


bibtype: inproceedings
filename: pagerank.bib
label: Ali:2006:ACC
author: R. Ali and M. M. S. Beg
title: Aggregating Content and Connectivity based Techniques for Measure of Web Search Quality
crossref: IEEE:2006:AAC
pages: 44--49
year: 2006
DOI: https://doi.org/10.1109/ADCOM.2006.4289853
bibdate: Thu May 06 15:35:59 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:35:59 ???
acknowledgement: ack-nhfb
rawauthor: R. Ali and M. M. S. Beg
rawtitle: Aggregating Content and Connectivity based Techniques for Measure of {Web} Search Quality
entry:
@InProceedings{Ali:2006:ACC,
  author =       "R. Ali and M. M. S. Beg",
  title =        "Aggregating Content and Connectivity based Techniques
                 for Measure of {Web} Search Quality",
  crossref =     "IEEE:2006:AAC",
  pages =        "44--49",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/ADCOM.2006.4289853",
  bibdate =      "Thu May 06 15:35:59 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


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

rawabstract:
A local graph partitioning algorithm finds a cut near a specified starting vertex, with a running time that depends largely on the size of the small side of the cut, rather than the size of the input graph. In this paper, we present a local partitioning algorithm using a variation of PageRank with a specified starting distribution. We derive a mixing result for PageRank vectors similar to that for random walks, and show that the ordering of the vertices produced by a PageRank vector reveals a cut with small conductance. In particular, we show that for any set C with conductance \Phiand volume k, a PageRank vector with a certain starting distribution can be used to produce a set with conductance O\left( {\sqrt {\Phi \log k} } \right). We present an improved algorithm for computing approximate PageRank vectors, which allows us to find such a set in time proportional to its size. In particular, we can find a cut with conductance at most \not o , whose small side has volume at least 2b, in time O\left( {2^b \log ^2 m/\not o^2 } \right) where m is the number of edges in the graph. By combining small sets found by this local partitioning algorithm, we obtain a cut with conductance \not o and approximately optimal balance in time O\left( {m\log ^4 m/\not o^2 } \right).

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


bibtype: article
filename: pagerank.bib
label: Avrachenkov:2006:ENL
author: Konstantin Avrachenkov and Nelly Litvak
title: The effect of new links on Google PageRank
journal: j-STOCH-MODELS
volume: 22
number: 2
pages: 319--331
year: 2006
CODEN: CSSME8
DOI: https://doi.org/10.1080/15326340600649052
ISSN: 1532-6349 OR 15326349
MRclass: 68U35 (90B18 91D30)
MRnumber: MR2220968 (2007f:68227)
MRreviewer: Mirel Co{\c{s}}ulschi
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
fjournal: Stochastic Models
ZMnumber: 1094.68005
acknowledgement: ack-nhfb
rawauthor: Konstantin Avrachenkov and Nelly Litvak
rawtitle: The effect of new links on {Google} {PageRank}
entry:
@Article{Avrachenkov:2006:ENL,
  author =       "Konstantin Avrachenkov and Nelly Litvak",
  title =        "The effect of new links on {Google} {PageRank}",
  journal =      j-STOCH-MODELS,
  volume =       "22",
  number =       "2",
  pages =        "319--331",
  year =         "2006",
  CODEN =        "CSSME8",
  DOI =          "https://doi.org/10.1080/15326340600649052",
  ISSN =         "1532-6349",
  MRclass =      "68U35 (90B18 91D30)",
  MRnumber =     "MR2220968 (2007f:68227)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1094.68005",
  acknowledgement = ack-nhfb,
  fjournal =     "Stochastic Models",
}


bibtype: article
filename: pagerank.bib
label: Avrachenkov:2006:PSF
author: Konstantin Avrachenkov and Dmitri Lebedev
title: PageRank of scale-free growing networks
journal: j-INTERNET-MATH
volume: 3
number: 2
pages: 207--231
year: 2006
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 05C80 (68M10 68R10 68U35)
MRnumber: MR2321830 (2008c:05162)
MRreviewer: Mirel Co{\c{s}}ulschi
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1204906139
fjournal: Internet Mathematics
ZMnumber: 1122.68406
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Konstantin Avrachenkov and Dmitri Lebedev
rawtitle: {PageRank} of scale-free growing networks
entry:
@Article{Avrachenkov:2006:PSF,
  author =       "Konstantin Avrachenkov and Dmitri Lebedev",
  title =        "{PageRank} of scale-free growing networks",
  journal =      j-INTERNET-MATH,
  volume =       "3",
  number =       "2",
  pages =        "207--231",
  year =         "2006",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "05C80 (68M10 68R10 68U35)",
  MRnumber =     "MR2321830 (2008c:05162)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1204906139",
  ZMnumber =     "1122.68406",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: inproceedings
filename: pagerank.bib
label: Baeza-Yates:2006:GPD
author: Ricardo Baeza-Yates and Paolo Boldi and Carlos Castillo
editor: ACM
title: Generalizing PageRank: damping functions for link-based ranking algorithms
publisher: pub-ACM
address: pub-ACM:adr
pages: 308--315
year: 2006
DOI: https://doi.org/10.1007/s10791-005-6993-5
ISBN: 1-59593-369-7 OR 1595933697
ISBN13: 978-1-59593-369-0 OR 9781595933690
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
keywords: link analysis; link-based ranking; web graphs
acknowledgement: ack-nhfb
rawauthor: Ricardo Baeza-Yates and Paolo Boldi and Carlos Castillo
raweditor: {ACM}
rawtitle: Generalizing {PageRank}: damping functions for link-based ranking algorithms
booktitle:
Annual ACM Conference on Research and Development in Information Retrieval Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

abstract:
This paper introduces a family of link-based ranking algorithms that propagate page importance through links. In these algorithms there is a damping function that decreases with distance, so a direct link implies more endorsement than a link through a long path. PageRank is the most widely known ranking function of this family.The main objective of this paper is to determine whether this family of ranking techniques has some interest per se, and how different choices for the damping function impact on rank quality and on convergence speed. Even though our results suggest that PageRank can be approximated with other simpler forms of rankings that may be computed more efficiently, our focus is of more speculative nature, in that it aims at separating the kernel of PageRank, that is, link-based importance propagation, from the way propagation decays over paths.We focus on three damping functions, having linear, exponential, and hyperbolic decay on the lengths of the paths. The exponential decay corresponds to PageRank, and the other functions are new. Our presentation includes algorithms, analysis, comparisons and experiments that study their behavior under different parameters in real Web graph data.Among other results, we show how to calculate a linear approximation that induces a page ordering that is almost identical to PageRank's using a fixed small number of iterations; comparisons were performed using Kendall's $ tau $ on large domain datasets.

rawabstract:
This paper introduces a family of link-based ranking algorithms that propagate page importance through links. In these algorithms there is a damping function that decreases with distance, so a direct link implies more endorsement than a link through a long path. PageRank is the most widely known ranking function of this family.The main objective of this paper is to determine whether this family of ranking techniques has some interest per se, and how different choices for the damping function impact on rank quality and on convergence speed. Even though our results suggest that PageRank can be approximated with other simpler forms of rankings that may be computed more efficiently, our focus is of more speculative nature, in that it aims at separating the kernel of PageRank, that is, link-based importance propagation, from the way propagation decays over paths.We focus on three damping functions, having linear, exponential, and hyperbolic decay on the lengths of the paths. The exponential decay corresponds to PageRank, and the other functions are new. Our presentation includes algorithms, analysis, comparisons and experiments that study their behavior under different parameters in real Web graph data.Among other results, we show how to calculate a linear approximation that induces a page ordering that is almost identical to PageRank's using a fixed small number of iterations; comparisons were performed using Kendall's $ \tau $ on large domain datasets.

rawbooktitle:
Annual ACM Conference on Research and Development in Information Retrieval Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

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


bibtype: inproceedings
filename: pagerank.bib
label: Bansal:2006:ADC
author: T. Bansal and P. Ghanshani and R. C. Joshi
title: An Application Dependent Communication Protocol for Wireless Sensor Networks
crossref: IEEE:2006:IIM
pages: 120--120
year: 2006
DOI: https://doi.org/10.1109/ICNICONSMCL.2006.46
bibdate: Thu May 06 16:24:09 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:24:09 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: T. Bansal and P. Ghanshani and R. C. Joshi
rawtitle: An Application Dependent Communication Protocol for Wireless Sensor Networks
entry:
@InProceedings{Bansal:2006:ADC,
  author =       "T. Bansal and P. Ghanshani and R. C. Joshi",
  title =        "An Application Dependent Communication Protocol for
                 Wireless Sensor Networks",
  crossref =     "IEEE:2006:IIM",
  pages =        "120--120",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/ICNICONSMCL.2006.46",
  bibdate =      "Thu May 06 16:24:09 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: article
filename: pagerank.bib
label: Bao:2006:LPD
author: Ying Bao and Yong Liu
title: Limit of PageRank with damping factor
journal: j-DYN-CONTIN-DISCR-IMPULS-B
volume: 13
number: 3-4
pages: 497--504
year: 2006
CODEN: DCDIS4
ISSN: 1492-8760 OR 14928760
MRclass: 68U35 (60J27 68P20 68W40)
MRnumber: MR2208501
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
fjournal: Dynamics of Continuous, Discrete & Impulsive Systems. Series B. Applications & Algorithms
ZMnumber: 1100.60040
acknowledgement: ack-nhfb
rawauthor: Ying Bao and Yong Liu
rawtitle: Limit of {PageRank} with damping factor
entry:
@Article{Bao:2006:LPD,
  author =       "Ying Bao and Yong Liu",
  title =        "Limit of {PageRank} with damping factor",
  journal =      j-DYN-CONTIN-DISCR-IMPULS-B,
  volume =       "13",
  number =       "3-4",
  pages =        "497--504",
  year =         "2006",
  CODEN =        "DCDIS4",
  ISSN =         "1492-8760",
  MRclass =      "68U35 (60J27 68P20 68W40)",
  MRnumber =     "MR2208501",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1100.60040",
  acknowledgement = ack-nhfb,
  fjournal =     "Dynamics of Continuous, Discrete \& Impulsive Systems.
                 Series B. Applications \& Algorithms",
}


bibtype: inproceedings
filename: pagerank.bib
label: Becchetti:2006:DPF
author: Luca Becchetti and Carlos Castillo
editor: ACM
title: The distribution of PageRank follows a power-law only for particular values of the damping factor
publisher: pub-ACM
address: pub-ACM:adr
pages: 941--942
year: 2006
DOI: https://doi.org/10.1016/S1389-1286(00)00063-3
ISBN: 1-59593-323-9 OR 1595933239
ISBN13: 978-1-59593-323-2 OR 9781595933232
bibdate: Sat May 8 18:33:08 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:08 MDT
keywords: pagerank distribution; web graph
acknowledgement: ack-nhfb
rawauthor: Luca Becchetti and Carlos Castillo
raweditor: {ACM}
rawtitle: The distribution of {PageRank} follows a power-law only for particular values of the damping factor
booktitle:
International World Wide Web Conference Proceedings of the 15th international conference on World Wide Web

abstract:
We show that the empirical distribution of the PageRank values in a large set of Web pages does not follow a power-law except for some particular choices of the damping factor. We argue that for a graph with an in-degree distribution following a power-law with exponent between 2.1 and 2.2, choosing a damping factor around 0.85 for PageRank yields a power-law distribution of its values. We suggest that power-law distributions of PageRank in Web graphs have been observed because the typical damping factor used in practice is between 0.85 and 0.90.

rawabstract:
We show that the empirical distribution of the PageRank values in a large set of Web pages does not follow a power-law except for some particular choices of the damping factor. We argue that for a graph with an in-degree distribution following a power-law with exponent between 2.1 and 2.2, choosing a damping factor around 0.85 for PageRank yields a power-law distribution of its values. We suggest that power-law distributions of PageRank in Web graphs have been observed because the typical damping factor used in practice is between 0.85 and 0.90.

rawbooktitle:
International World Wide Web Conference Proceedings of the 15th international conference on World Wide Web

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


bibtype: article
filename: pagerank.bib
label: Berkhin:2006:BCA
author: Pavel Berkhin
title: Bookmark-coloring algorithm for personalized PageRank computing
journal: j-INTERNET-MATH
volume: 3
number: 1
pages: 41--62
year: 2006
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 68U35 (68M10 68R10); 68P10 68M10 68P20 68W05
MRnumber: MR2283883 (2007k:68134)
MRreviewer: Mirel Co{\c{s}}ulschi
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1175266367
fjournal: Internet Mathematics
ZMnumber: 1113.68375
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Pavel Berkhin
rawtitle: Bookmark-coloring algorithm for personalized {PageRank} computing
entry:
@Article{Berkhin:2006:BCA,
  author =       "Pavel Berkhin",
  title =        "Bookmark-coloring algorithm for personalized
                 {PageRank} computing",
  journal =      j-INTERNET-MATH,
  volume =       "3",
  number =       "1",
  pages =        "41--62",
  year =         "2006",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (68M10 68R10); 68P10 68M10 68P20 68W05",
  MRnumber =     "MR2283883 (2007k:68134)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1175266367",
  ZMnumber =     "1113.68375",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: article
filename: pagerank.bib
label: Boldi:2006:GFG
author: Paolo Boldi and Violetta Lonati and Massimo Santini and Sebastiano Vigna
title: Graph fibrations, graph isomorphism, and PageRank
journal: j-INFORM-THEOR-APPL
volume: 40
number: 2
pages: 227--253
year: 2006
CODEN: RSITD7, RITAE4
DOI: https://doi.org/10.1051/ita:2006004
ISSN: 0988-3754 (print), 1290-385X (electronic) OR 09883754 (print), 1290385X (electronic)
ISSNL: 0988-3754 OR 09883754
MRclass: 68U35 (05C60 60J10 60J20 68R10 94C15)
MRnumber: MR2252637 (2007h:68204)
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
fjournal: Theoretical Informatics and Applications. Informatique Theorique et Applications
ZMnumber: 1112.68002
acknowledgement: ack-nhfb
rawauthor: Paolo Boldi and Violetta Lonati and Massimo Santini and Sebastiano Vigna
rawtitle: Graph fibrations, graph isomorphism, and {PageRank}
entry:
@Article{Boldi:2006:GFG,
  author =       "Paolo Boldi and Violetta Lonati and Massimo Santini
                 and Sebastiano Vigna",
  title =        "Graph fibrations, graph isomorphism, and {PageRank}",
  journal =      j-INFORM-THEOR-APPL,
  volume =       "40",
  number =       "2",
  pages =        "227--253",
  year =         "2006",
  CODEN =        "RSITD7, RITAE4",
  DOI =          "https://doi.org/10.1051/ita:2006004",
  ISSN =         "0988-3754 (print), 1290-385X (electronic)",
  ISSN-L =       "0988-3754",
  MRclass =      "68U35 (05C60 60J10 60J20 68R10 94C15)",
  MRnumber =     "MR2252637 (2007h:68204)",
  bibdate =      "Wed May 5 19:28:01 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1112.68002",
  acknowledgement = ack-nhfb,
  fjournal =     "Theoretical Informatics and Applications. Informatique
                 Th\'eorique et Applications",
}


bibtype: article
filename: pagerank.bib
label: Bollen:2006:JS
author: Johan Bollen and Marko A. Rodriquez and Herbert Van de Sompel
title: Journal status
journal: j-SCIENTOMETRICS
volume: 69
number: 3
pages: 669--687
month: dec
monthnumber: 12
year: 2006
CODEN: SCNTDX
DOI: https://doi.org/10.1007/s11192-006-0176-z
ISSN: 0138-9130 (print), 1588-2861 (electronic) OR 01389130 (print), 15882861 (electronic)
ISSNL: 0138-9130 OR 01389130
bibdate: Tue Aug 11 17:28:42 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:28:42 ???
URL: http://link.springer.com/article/10.1007/s11192-006-0176-z
fjournal: Scientometrics
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11192
rawauthor: Johan Bollen and Marko A. Rodriquez and Herbert {Van de Sompel}
rawtitle: Journal status
entry:
@Article{Bollen:2006:JS,
  author =       "Johan Bollen and Marko A. Rodriquez and Herbert {Van
                 de Sompel}",
  title =        "Journal status",
  journal =      j-SCIENTOMETRICS,
  volume =       "69",
  number =       "3",
  pages =        "669--687",
  month =        dec,
  year =         "2006",
  CODEN =        "SCNTDX",
  DOI =          "https://doi.org/10.1007/s11192-006-0176-z",
  ISSN =         "0138-9130 (print), 1588-2861 (electronic)",
  ISSN-L =       "0138-9130",
  bibdate =      "Tue Aug 11 17:28:42 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://link.springer.com/article/10.1007/s11192-006-0176-z",
  acknowledgement = ack-nhfb,
  fjournal =     "Scientometrics",
  journal-URL =  "http://link.springer.com/journal/11192",
}


bibtype: article
filename: pagerank.bib
label: Brezinski:2006:PVP
author: Claude Brezinski and Michela Redivo-Zaglia
title: The PageRank vector: properties, computation, approximation, and acceleration
journal: j-SIAM-J-MAT-ANA-APPL
volume: 28
number: 2
pages: 551--575
year: 2006
CODEN: SJMAEL
DOI: https://doi.org/10.1137/050626612
ISSN: 0895-4798 (print), 1095-7162 (electronic) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
MRclass: 68U35 (65F15)
MRnumber: MR2255342 (2007h:68205)
MRreviewer: Mirel Co{\c{s}}ulschi
bibdate: Wed May 5 19:28:01 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:01 ???
fjournal: SIAM Journal on Matrix Analysis and Applications
ZMnumber: 1116.65042
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
rawauthor: Claude Brezinski and Michela Redivo-Zaglia
rawtitle: The {PageRank} vector: properties, computation, approximation, and acceleration
abstract:
An important problem in Web search is determining the importance of each page. After introducing the main characteristics of this problem, we will see that, from the mathematical point of view, it could be solved by computing the left principal eigenvector (the PageRank vector) of a matrix related to the structure of the Web by using the power method. We will give expressions of the PageRank vector and study the mathematical properties of the power method. Various Pade-style approximations of the PageRank vector will be given. Since the convergence of the power method is slow, it has to be accelerated. This problem will be discussed. Recently, several acceleration methods were proposed. We will give a theoretical justification for these methods. In particular, we will generalize the recently proposed Quadratic Extrapolation, and we interpret it on the basis of the method of moments of Vorobyev, and as a Krylov subspace method. Acceleration results are given for the various epsilon -algorithms, and for the E -algorithm. Other algorithms for this problem are also discussed.

rawabstract:
An important problem in Web search is determining the importance of each page. After introducing the main characteristics of this problem, we will see that, from the mathematical point of view, it could be solved by computing the left principal eigenvector (the PageRank vector) of a matrix related to the structure of the Web by using the power method. We will give expressions of the PageRank vector and study the mathematical properties of the power method. Various Pad{\'e}-style approximations of the PageRank vector will be given. Since the convergence of the power method is slow, it has to be accelerated. This problem will be discussed. Recently, several acceleration methods were proposed. We will give a theoretical justification for these methods. In particular, we will generalize the recently proposed Quadratic Extrapolation, and we interpret it on the basis of the method of moments of Vorobyev, and as a Krylov subspace method. Acceleration results are given for the various epsilon -algorithms, and for the E -algorithm. Other algorithms for this problem are also discussed.

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


bibtype: article
filename: pagerank.bib
label: Brinkmeier:2006:PR
author: Michael Brinkmeier
title: PageRank revisited
journal: j-TOIT
volume: 6
number: 3
pages: 282--301
month: aug
monthnumber: 8
year: 2006
DOI: https://doi.org/10.1145/1151087.1151090
ISSN: 1533-5399 (print), 1557-6051 (electronic) OR 15335399 (print), 15576051 (electronic)
ISSNL: 1533-5399 OR 15335399
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: http://www.acm.org/pubs/contents/journals/toit/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
URL: https://www.math.utah.edu/pub/tex/bib/toit.bib
fjournal: ACM Transactions on Internet Technology (TOIT)
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J780
rawauthor: Michael Brinkmeier
rawtitle: {PageRank} revisited
abstract:
PageRank, one part of the search engine Google, is one of the most prominent link-based rankings of documents in the World Wide Web. Usually it is described as a Markov chain modeling a specific random surfer. In this article, an alternative representation as a power series is given. Nonetheless, it is possible to interpret the values as probabilities in a random surfer setting, differing from the usual one. Using the new description we restate and extend some results concerning the convergence of the standard iteration used for PageRank. Furthermore we take a closer look at sinks and sources, leading to some suggestions for faster implementations.

keywords:
Dynamical update; link-analysis; Markov chain; Pagerank; personalization; random surfer; ranking algorithm; Web graph; Web page scoring; Web search; World Wide Web

rawabstract:
PageRank, one part of the search engine Google, is one of the most prominent link-based rankings of documents in the World Wide Web. Usually it is described as a Markov chain modeling a specific random surfer. In this article, an alternative representation as a power series is given. Nonetheless, it is possible to interpret the values as probabilities in a random surfer setting, differing from the usual one. Using the new description we restate and extend some results concerning the convergence of the standard iteration used for PageRank. Furthermore we take a closer look at sinks and sources, leading to some suggestions for faster implementations.

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


bibtype: article
filename: pagerank.bib
label: Broder:2006:EPA
author: A. Z. Broder and R. Lempel and F. Maghoul and J. Pedersen
title: Efficient PageRank approximation via graph aggregation
journal: j-INF-RETR
volume: 9
number: 2
pages: 123--138
month: mar
monthnumber: 3
year: 2006
CODEN: IFRTFY
DOI: https://doi.org/10.1145/775152.775203
ISSN: 1386-4564 (print), 1573-7659 (electronic) OR 13864564 (print), 15737659 (electronic)
ISSNL: 1386-4564 OR 13864564
bibdate: Sat May 8 18:33:07 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:07 MDT
fjournal: Information Retrieval
keywords: Citation and link analysis; Web IR
acknowledgement: ack-nhfb
rawauthor: A. Z. Broder and R. Lempel and F. Maghoul and J. Pedersen
rawtitle: Efficient {PageRank} approximation via graph aggregation
abstract:
We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. The basic idea is to partition the graph into classes of quasi-equivalent vertices, to project the page-based random walk to be approximated onto those classes, and to compute the stationary probability distribution of the resulting class-based random walk. From this distribution we can quickly reconstruct a distribution on pages. In particular, our framework can approximate the well-known PageRank distribution by setting the classes according to the set of pages on each Web host. par We experimented on a Web-graph containing over 1.4 billion pages and over 6.6 billion links from a crawl of the Web conducted by AltaVista in September 2003. We were able to produce a ranking that has Spearman rank-order correlation of 0.95 with respect to PageRank. The clock time required by a simplistic implementation of our method was less than half the time required by a highly optimized implementation of PageRank, implying that larger speedup factors are probably possible.

rawabstract:
We present a framework for approximating random-walk based probability distributions over Web pages using graph aggregation. The basic idea is to partition the graph into classes of quasi-equivalent vertices, to project the page-based random walk to be approximated onto those classes, and to compute the stationary probability distribution of the resulting class-based random walk. From this distribution we can quickly reconstruct a distribution on pages. In particular, our framework can approximate the well-known PageRank distribution by setting the classes according to the set of pages on each Web host. \par We experimented on a Web-graph containing over 1.4 billion pages and over 6.6 billion links from a crawl of the Web conducted by AltaVista in September 2003. We were able to produce a ranking that has Spearman rank-order correlation of 0.95 with respect to PageRank. The clock time required by a simplistic implementation of our method was less than half the time required by a highly optimized implementation of PageRank, implying that larger speedup factors are probably possible.

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

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


bibtype: article
filename: pagerank.bib
label: Bryan:2006:ELA
author: Kurt Bryan and Tanya Leise
title: The $25,000,000,000 Eigenvector: The Linear Algebra behind Google
journal: j-SIAM-REVIEW
volume: 48
number: 3
pages: 569--581
year: 2006
CODEN: SIREAD
DOI: https://doi.org/10.1137/050623280
ISSN: 0036-1445 (print), 1095-7200 (electronic) OR 00361445 (print), 10957200 (electronic)
ISSNL: 0036-1445 OR 00361445
bibdate: Tue Dec 2 17:02:29 MST 2008
bibtimestamp: 2008.12.02 17:02:29 MST
URL: https://www.math.utah.edu/pub/tex/bib/siamreview.bib
fjournal: SIAM Review
keywords: PageRank; singular-value decomposition; SVD
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sirev
rawauthor: Kurt Bryan and Tanya Leise
rawtitle: The \$25,000,000,000 Eigenvector: The Linear Algebra behind {Google}
bibsource:
http://epubs.siam.org/sam-bin/dbq/toc/SIREV/48/3; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

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


bibtype: inproceedings
filename: pagerank.bib
label: Chongsuntornsri:2006:ATT
author: Aekkasit Chongsuntornsri and Ohm Sornil
booktitle: ISCIT '06: International Symposium on Communications and Information Technologies (2006)
title: An Automatic Thai Text Summarization Using Topic Sensitive PageRank
crossref: IEEE:2006:CIT
pages: 547--552
year: 2006
DOI: https://doi.org/10.1109/ISCIT.2006.340009
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=4141445
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4141327
rawauthor: Aekkasit Chongsuntornsri and Ohm Sornil
rawbooktitle: {ISCIT '06: International Symposium on Communications and Information Technologies (2006)}
rawtitle: An Automatic {Thai} Text Summarization Using Topic Sensitive {PageRank}
entry:
@InProceedings{Chongsuntornsri:2006:ATT,
  author =       "Aekkasit Chongsuntornsri and Ohm Sornil",
  booktitle =    "{ISCIT '06: International Symposium on Communications
                 and Information Technologies (2006)}",
  title =        "An Automatic {Thai} Text Summarization Using Topic
                 Sensitive {PageRank}",
  crossref =     "IEEE:2006:CIT",
  pages =        "547--552",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/ISCIT.2006.340009",
  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=4141445",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4141327",
}


bibtype: inproceedings
filename: pagerank.bib
label: Davis:2006:EGP
author: Jason V. Davis and Inderjit S. Dhillon
editor: ACM
title: Estimating the global PageRank of Web communities
publisher: pub-ACM
address: pub-ACM:adr
pages: 116--125
year: 2006
DOI: https://doi.org/10.1145/1099554.1099583
ISBN: 1-59593-339-5 OR 1595933395
ISBN13: 978-1-59593-339-3 OR 9781595933393
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
keywords: Markov chain; page rank; stochastic complementation
acknowledgement: ack-nhfb
rawauthor: Jason V. Davis and Inderjit S. Dhillon
raweditor: {ACM}
rawtitle: Estimating the global {PageRank} of {Web} communities
booktitle:
International Conference on Knowledge Discovery and Data Mining Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

abstract:
Localized search engines are small-scale systems that index a particular community on the web. They offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build, and can provide more precise and complete search capability over their relevant domains. One disadvantage such systems have over large-scale search engines is the lack of global PageRank values. Such information is needed to assess the value of pages in the localized search domain within the context of the web as a whole. In this paper, we present well-motivated algorithms to estimate the global PageRank values of a local domain. The algorithms are all highly scalable in that, given a local domain of size n, they use O(n) resources that include computation time, bandwidth, and storage. We test our methods across a variety of localized domains, including site-specific domains and topic-specific domains. We demonstrate that by crawling as few as n or 2n additional pages, our methods can give excellent global PageRank estimates.

rawabstract:
Localized search engines are small-scale systems that index a particular community on the web. They offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build, and can provide more precise and complete search capability over their relevant domains. One disadvantage such systems have over large-scale search engines is the lack of global PageRank values. Such information is needed to assess the value of pages in the localized search domain within the context of the web as a whole. In this paper, we present well-motivated algorithms to estimate the global PageRank values of a local domain. The algorithms are all highly scalable in that, given a local domain of size n, they use O(n) resources that include computation time, bandwidth, and storage. We test our methods across a variety of localized domains, including site-specific domains and topic-specific domains. We demonstrate that by crawling as few as n or 2n additional pages, our methods can give excellent global PageRank estimates.

rawbooktitle:
International Conference on Knowledge Discovery and Data Mining Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

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


bibtype: inproceedings
filename: pagerank.bib
label: DeLong:2006:CAR
author: Colin DeLong and Sandeep Mane and Jaideep Srivastava
title: Concept-Aware Ranking: Teaching an Old Graph New Moves
crossref: Clifton:2006:SIC
pages: 80--88
year: 2006
DOI: https://doi.org/10.1109/ICDMW.2006.49
bibdate: Thu May 06 15:42:10 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:42:10 ???
acknowledgement: ack-nhfb
rawauthor: Colin DeLong and Sandeep Mane and Jaideep Srivastava
rawtitle: Concept-Aware Ranking: Teaching an Old Graph New Moves
entry:
@InProceedings{DeLong:2006:CAR,
  author =       "Colin DeLong and Sandeep Mane and Jaideep Srivastava",
  title =        "Concept-Aware Ranking: Teaching an Old Graph New
                 Moves",
  crossref =     "Clifton:2006:SIC",
  pages =        "80--88",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/ICDMW.2006.49",
  bibdate =      "Thu May 06 15:42:10 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Desikan:2006:DCA
author: Prasanna Kumar Desikan and Nishith Pathak and Jaideep Srivastava and Vipin Kumar
editor: ACM
booktitle: Proceedings of the 6th international conference on Web engineering
title: Divide and conquer approach for efficient PageRank computation
volume: 263
publisher: pub-ACM
address: pub-ACM:adr
pages: 233--240
year: 2006
DOI: https://doi.org/10.1145/988672.988714
ISBN: 1-59593-352-2 OR 1595933522
ISBN13: 978-1-59593-352-2 OR 9781595933522
bibdate: Sat May 8 18:33:08 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:08 MDT
keywords: efficient computation; graph partitioning; PageRank; ranking measures
acknowledgement: ack-nhfb
rawauthor: Prasanna Kumar Desikan and Nishith Pathak and Jaideep Srivastava and Vipin Kumar
rawbooktitle: {Proceedings of the 6th international conference on Web engineering}
raweditor: {ACM}
rawtitle: Divide and conquer approach for efficient {PageRank} computation
abstract:
PageRank is a popular ranking metric for large graphs such as the World Wide Web. Current research techniques for improving computational efficiency of PageRank have focused on improving the I/O cost, convergence and parallelizing the computation process. In this paper, we propose a divide and conquer strategy for efficient computation of PageRank. The strategy is different from contemporary improvements in that it can be combined with any existing enhancements to PageRank, giving way to an entire class of more efficient algorithms. We present a novel graph-partitioning technique for dividing the graph into subgraphs, on which computation can be performed independently. This approach has two significant benefits. Firstly, since the approach focuses on work-reduction, it can be combined with any existing enhancements to PageRank. Secondly, the proposed approach leads naturally into developing an incremental approach for computation of such ranking metrics given that these large graphs evolve over a period of time. The partitioning technique is both lossless and independent of the type (variant) of PageRank computation algorithm used. The experimental results for a static single graph (graph at a single time instance) as well as for the incremental computation in case of evolving graphs, illustrate the utility of our novel partitioning approach. The proposed approach can also be applied for the computation of any other metric based on first order Markov chain model.

rawabstract:
PageRank is a popular ranking metric for large graphs such as the World Wide Web. Current research techniques for improving computational efficiency of PageRank have focused on improving the I/O cost, convergence and parallelizing the computation process. In this paper, we propose a divide and conquer strategy for efficient computation of PageRank. The strategy is different from contemporary improvements in that it can be combined with any existing enhancements to PageRank, giving way to an entire class of more efficient algorithms. We present a novel graph-partitioning technique for dividing the graph into subgraphs, on which computation can be performed independently. This approach has two significant benefits. Firstly, since the approach focuses on work-reduction, it can be combined with any existing enhancements to PageRank. Secondly, the proposed approach leads naturally into developing an incremental approach for computation of such ranking metrics given that these large graphs evolve over a period of time. The partitioning technique is both lossless and independent of the type (variant) of PageRank computation algorithm used. The experimental results for a static single graph (graph at a single time instance) as well as for the incremental computation in case of evolving graphs, illustrate the utility of our novel partitioning approach. The proposed approach can also be applied for the computation of any other metric based on first order Markov chain model.

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


bibtype: article
filename: pagerank.bib
label: Gleich:2006:APP
author: David Gleich and Marzia Polito
title: Approximating personalized PageRank with minimal use of web graph data
journal: j-INTERNET-MATH
volume: 3
number: 3
pages: 257--294
year: 2006
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 68U35 (05C85 05C90 68M10 68R10)
MRnumber: MR2372544 (2008m:68217)
MRreviewer: Mirel Co{\c{s}}ulschi
bibdate: Wed May 5 19:28:02 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:02 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1204906158
fjournal: Internet Mathematics
ZMnumber: 1147.68350
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: David Gleich and Marzia Polito
rawtitle: Approximating personalized {PageRank} with minimal use of web graph data
entry:
@Article{Gleich:2006:APP,
  author =       "David Gleich and Marzia Polito",
  title =        "Approximating personalized {PageRank} with minimal use
                 of web graph data",
  journal =      j-INTERNET-MATH,
  volume =       "3",
  number =       "3",
  pages =        "257--294",
  year =         "2006",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (05C85 05C90 68M10 68R10)",
  MRnumber =     "MR2372544 (2008m:68217)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:02 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1204906158",
  ZMnumber =     "1147.68350",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: article
filename: pagerank.bib
label: Golub:2006:ATA
author: G. H. Golub and C. Greif
title: An Arnoldi-type algorithm for computing PageRank
journal: j-BIT
volume: 46
number: 4
pages: 759--771
year: 2006
CODEN: BITTEL, NBITAB
DOI: https://doi.org/10.1007/s10543-006-0091-y
ISSN: 0006-3835 (print), 1572-9125 (electronic) OR 00063835 (print), 15729125 (electronic)
ISSNL: 0006-3835 OR 00063835
MRclass: 65F15
MRnumber: MR2285207 (2008a:65073)
MRreviewer: Jan Mandel
bibdate: Wed May 5 19:28:02 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:02 ???
fjournal: BIT. Numerical Mathematics
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/10543
rawauthor: G. H. Golub and C. Greif
rawtitle: An {Arnoldi}-type algorithm for computing {PageRank}
entry:
@Article{Golub:2006:ATA,
  author =       "G. H. Golub and C. Greif",
  title =        "An {Arnoldi}-type algorithm for computing {PageRank}",
  journal =      j-BIT,
  volume =       "46",
  number =       "4",
  pages =        "759--771",
  year =         "2006",
  CODEN =        "BITTEL, NBITAB",
  DOI =          "https://doi.org/10.1007/s10543-006-0091-y",
  ISSN =         "0006-3835 (print), 1572-9125 (electronic)",
  ISSN-L =       "0006-3835",
  MRclass =      "65F15",
  MRnumber =     "MR2285207 (2008a:65073)",
  MRreviewer =   "Jan Mandel",
  bibdate =      "Wed May 5 19:28:02 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "BIT. Numerical Mathematics",
  journal-URL =  "http://link.springer.com/journal/10543",
}


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

rawabstract:
The grid deals with large scale and ever-expanding environment which contains million of users and resources. For this reason, resource selection has been a challenging task especially in meeting user's demand for a quality of service (QoS). A quality of service is the ability to serve a job by providing quality and reliable resource in fulfilling the user's need. Quality and reliable resource selections naturally yield excellent and quality results. The background of the users and where the resource belongs to are important in determining the quality of a resource. This paper concerns with efficient and quality-based resource discovery using Condor ClassAd and PageRank technique in order to achieve a quality resource matching. The paper discusses how quality of users and resources are determined and considered in the discovery process prior to allocating jobs to resources.

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


bibtype: inproceedings
filename: pagerank.bib
label: Huang:2006:TPA
author: Decai Huang and Huachun Qi and Yuan Yuan and Yue-feng Zheng
booktitle: WCICA 2006: The Sixth World Congress on Intelligent Control and Automation
title: TC-PageRank Algorithm Based on Topic Correlation
crossref: IEEE:2006:WSW
volume: 2
pages: 5943--5946
year: 2006
DOI: https://doi.org/10.1109/WCICA.2006.1714219
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=1714219
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11210
rawauthor: Decai Huang and Huachun Qi and Yuan Yuan and Yue-feng Zheng
rawbooktitle: {WCICA 2006: The Sixth World Congress on Intelligent Control and Automation}
rawtitle: {TC-PageRank} Algorithm Based on Topic Correlation
entry:
@InProceedings{Huang:2006:TPA,
  author =       "Decai Huang and Huachun Qi and Yuan Yuan and Yue-feng
                 Zheng",
  booktitle =    "{WCICA 2006: The Sixth World Congress on Intelligent
                 Control and Automation}",
  title =        "{TC-PageRank} Algorithm Based on Topic Correlation",
  crossref =     "IEEE:2006:WSW",
  volume =       "2",
  pages =        "5943--5946",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/WCICA.2006.1714219",
  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=1714219",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11210",
}


bibtype: article
filename: pagerank.bib
label: Ipsen:2006:CAP
author: Ilse C. F. Ipsen and Steve Kirkland
title: Convergence analysis of a PageRank updating algorithm by Langville and Meyer
journal: j-SIAM-J-MAT-ANA-APPL
volume: 27
number: 4
pages: 952--967
year: 2006
CODEN: SJMAEL
DOI: https://doi.org/10.1137/S0895479804439808
ISSN: 0895-4798 (print), 1095-7162 (electronic) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
MRclass: 65F30 (15A51 68U35); 65F15 65F10 15A18 15A42 65C40 15A51 68P10 60J22
MRnumber: MR2205606 (2006i:65070)
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
fjournal: SIAM Journal on Matrix Analysis and Applications
ZMnumber: 1108.65030
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
rawauthor: Ilse C. F. Ipsen and Steve Kirkland
rawtitle: Convergence analysis of a {PageRank} updating algorithm by {Langville} and {Meyer}
entry:
@Article{Ipsen:2006:CAP,
  author =       "Ilse C. F. Ipsen and Steve Kirkland",
  title =        "Convergence analysis of a {PageRank} updating
                 algorithm by {Langville} and {Meyer}",
  journal =      j-SIAM-J-MAT-ANA-APPL,
  volume =       "27",
  number =       "4",
  pages =        "952--967",
  year =         "2006",
  CODEN =        "SJMAEL",
  DOI =          "https://doi.org/10.1137/S0895479804439808",
  ISSN =         "0895-4798 (print), 1095-7162 (electronic)",
  ISSN-L =       "0895-4798",
  MRclass =      "65F30 (15A51 68U35); 65F15 65F10 15A18 15A42 65C40
                 15A51 68P10 60J22",
  MRnumber =     "MR2205606 (2006i:65070)",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1108.65030",
  acknowledgement = ack-nhfb,
  fjournal =     "SIAM Journal on Matrix Analysis and Applications",
  journal-URL =  "http://epubs.siam.org/simax",
}


bibtype: article
filename: pagerank.bib
label: Ipsen:2006:MPA
author: Ilse C. F. Ipsen and Rebecca S. Wills
title: Mathematical properties and analysis of Google's PageRank
journal: Bol. Soc. Esp. Mat. Apl. S$vec rm e$MA
volume: 34
pages: 191--196
year: 2006
ISSN: 1575-9822 OR 15759822
MRclass: 65F15 (15A51)
MRnumber: MR2296216
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
fjournal: Boleti n de la Sociedad Espanola de Matematica Aplicada. S$vec rm e$MA
acknowledgement: ack-nhfb
rawauthor: Ilse C. F. Ipsen and Rebecca S. Wills
rawtitle: Mathematical properties and analysis of {Google}'s {PageRank}
entry:
@Article{Ipsen:2006:MPA,
  author =       "Ilse C. F. Ipsen and Rebecca S. Wills",
  title =        "Mathematical properties and analysis of {Google}'s
                 {PageRank}",
  journal =      "Bol. Soc. Esp. Mat. Apl. S$\vec{\rm e}$MA",
  volume =       "34",
  pages =        "191--196",
  year =         "2006",
  CODEN =        "????",
  ISSN =         "1575-9822",
  MRclass =      "65F15 (15A51)",
  MRnumber =     "MR2296216",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Bolet\'\i n de la Sociedad Espa\~nola de Matem\'atica
                 Aplicada. S$\vec{\rm e}$MA",
}


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

rawabstract:
Recently, it is getting more frequent to search not Web contents but local contents, e.g., by Google Desktop Search. Google succeeded in the Web search because of its PageRank algorithm for the ranking of the search results. PageRank estimates the quality of Web pages based on their popularity, which in turn is estimated by the number and the quality of pages referring to them through hyperlinks. This algorithm, however, is not applicable when we search local contents without link structure, such as text data. In this research, we propose a method to estimate the quality of local contents without link structure by using the PageRank values of Web contents similar to them. Based on this estimation, we can rank the desktop search results. Furthermore, this method enables us to search contents across different resources such as Web contents and local contents. In this paper, we applied this method to Web contents, calculated the scores that estimate their quality, and we compare them with their page quality scores by PageRank.

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


bibtype: article
filename: pagerank.bib
label: Kirkland:2006:CES
author: S. Kirkland
title: Conditioning of the entries in the stationary vector of a Google-type matrix
journal: j-LINEAR-ALGEBRA-APPL
volume: 418
number: 2--3
pages: 665--681
day: 15
month: oct
monthnumber: 10
year: 2006
CODEN: LAAPAW
DOI: https://doi.org/10.1016/j.laa.2006.03.007
ISSN: 0024-3795 (print), 1873-1856 (electronic) OR 00243795 (print), 18731856 (electronic)
ISSNL: 0024-3795 OR 00243795
bibdate: Wed Mar 30 14:18:57 MDT 2011
bibtimestamp: 2011.03.30 14:18:57 MDT
fjournal: Linear Algebra and its Applications
keywords: condition number; PageRank; stationary vector; stochastic matrix
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00243795
rawauthor: S. Kirkland
rawtitle: Conditioning of the entries in the stationary vector of a {Google}-type matrix
bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www.sciencedirect.com/science/journal/00243795

entry:
@Article{Kirkland:2006:CES,
  author =       "S. Kirkland",
  title =        "Conditioning of the entries in the stationary vector
                 of a {Google}-type matrix",
  journal =      j-LINEAR-ALGEBRA-APPL,
  volume =       "418",
  number =       "2--3",
  pages =        "665--681",
  day =          "15",
  month =        oct,
  year =         "2006",
  CODEN =        "LAAPAW",
  DOI =          "https://doi.org/10.1016/j.laa.2006.03.007",
  ISSN =         "0024-3795 (print), 1873-1856 (electronic)",
  ISSN-L =       "0024-3795",
  bibdate =      "Wed Mar 30 14:18:57 MDT 2011",
  bibsource =    "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",
  keywords =     "condition number; PageRank; stationary vector;
                 stochastic matrix",
}


bibtype: inproceedings
filename: pagerank.bib
label: Kozakiewicz:2006:TLA
author: A. Kozakiewicz and A. Karbowskr
title: A Two-Level Approach to Building a Campus Grid
crossref: IEEE:2006:ISP
pages: 121--126
year: 2006
DOI: https://doi.org/10.1109/PARELEC.2006.11
bibdate: Thu May 06 15:58:28 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:58:28 ???
acknowledgement: ack-nhfb
rawauthor: A. Kozakiewicz and A. Karbowskr
rawtitle: A Two-Level Approach to Building a Campus Grid
entry:
@InProceedings{Kozakiewicz:2006:TLA,
  author =       "A. Kozakiewicz and A. Karbowskr",
  title =        "A Two-Level Approach to Building a Campus Grid",
  crossref =     "IEEE:2006:ISP",
  pages =        "121--126",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/PARELEC.2006.11",
  bibdate =      "Thu May 06 15:58:28 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: book
filename: pagerank.bib
label: Langville:2006:GPB
author: Amy N. Langville and Carl D. (Carl Dean) Meyer
title: Google's PageRank and beyond: the science of search engine rankings
publisher: pub-PRINCETON
address: pub-PRINCETON:adr
pages: x + 224
year: 2006
ISBN: 0-691-12202-4 (hardcover) OR 0691122024 (hardcover)
ISBN13: 978-0-691-12202-1 (hardcover) OR 9780691122021 (hardcover)
LCCN: TK5105.885.G66 L36 2006
MRclass: 68-02 (00-01 00A05 15A18 68U35)
MRnumber: MR2262054 (2007h:68002)
MRreviewer: Jiu Ding
bibdate: Fri Oct 23 16:04:57 MDT 2009
bibtimestamp: 2009.10.23 16:04:57 MDT
ZMnumber: 1104.68042
acknowledgement: ack-nhfb
rawauthor: Amy N. Langville and Carl D. (Carl Dean) Meyer
rawtitle: {Google}'s {PageRank} and beyond: the science of search engine rankings
bibsource:
https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib; https://www.math.utah.edu/pub/tex/bib/master.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.loc.gov:7090/Voyager

URL:
http://www.loc.gov/catdir/enhancements/fy0654/2005938841-b.html; http://www.loc.gov/catdir/enhancements/fy0654/2005938841-d.html; http://www.loc.gov/catdir/enhancements/fy0668/2005938841-t.html

subject:
Google; Web search engines; Web sites; Ratings; Mathematics; Internet searching; World Wide Web; Subject access

TOC:
Preface / ix 1. Introduction to Web Search Engines / 1 1.1 A Short History of Information Retrieval / 1 1.2 An Overview of Traditional Information Retrieval / 5 1.3 Web Information Retrieval / 9 2. Crawling, Indexing, and Query Processing / 15 2.1 Crawling / 15 2.2 The Content Index / 19 2.3 Query Processing / 21 3. Ranking Webpages by Popularity / 25 3.1 The Scene in 1998 / 25 3.2 Two Theses / 26 3.3 Query-Independence / 30 4. The Mathematics of Google's PageRank / 31 4.1 The Original Summation Formula for PageRank / 32 4.2 Matrix Representation of the Summation Equations / 33 4.3 Problems with the Iterative Process / 34 4.4 A Little Markov Chain Theory / 36 4.5 Early Adjustments to the Basic Model / 36 4.6 Computation of the PageRank Vector / 39 4.7 Theorem and Proof for Spectrum of the Google Matrix / 45 5. Parameters in the PageRank Model / 47 5.1 The $alpha $ Factor / 47 5.2 The Hyperlink Matrix $H$ / 48 5.3 The Teleportation Matrix $E$ / 49 6. The Sensitivity of PageRank / 57 6.1 Sensitivity with respect to $alpha $ / 57 6.2 Sensitivity with respect to $H$ / 62 6.3 Sensitivity with respect to $v^T$ / 63 6.4 Other Analyses of Sensitivity / 63 6.5 Sensitivity Theorems and Proofs / 66 7. The PageRank Problem as a Linear System / 71 7.1 Properties of $(I - $alpha $ S)$ / 71 7.2 Properties of $(I - $alpha $ H)$ / 72 7.3 Proof of the PageRank Sparse Linear System / 73 8. Issues in Large-Scale Implementation of PageRank / 75 8.1 Storage Issues / 75 8.2 Convergence Criterion / 79 8.3 Accuracy / 79 8.4 Dangling Nodes / 80 8.5 Back Button Modeling / 84 9. Accelerating the Computation of PageRank / 89 9.1 An Adaptive Power Method / 89 9.2 Extrapolation / 90 9.3 Aggregation / 94 9.4 Other Numerical Methods / 97 10. Updating the PageRank Vector / 99 10.1 The Two Updating Problems and their History / 100 10.2 Restarting the Power Method / 101 10.3 Approximate Updating Using Approximate Aggregation / 102 10.4 Exact Aggregation / 104 10.5 Exact vs. Approximate Aggregation / 105 10.6 Updating with Iterative Aggregation / 107 10.7 Determining the Partition / 109 10.8 Conclusions / 111 11. The HITS Method for Ranking Webpages / 115 11.1 The HITS Algorithm / 115 11.2 HITS Implementation / 117 11.3 HITS Convergence / 119 11.4 HITS Example / 120 11.5 Strengths and Weaknesses of HITS / 122 11.6 HITS's Relationship to Bibliometrics / 123 11.7 Query-Independent HITS / 124 11.8 Accelerating HITS / 126 11.9 HITS Sensitivity / 126 12. Other Link Methods for Ranking Webpages / 131 12.1 SALSA / 131 12.2 Hybrid Ranking Methods / 135 12.3 Rankings based on Traffic Flow / 136 13. The Future of Web Information Retrieval / 139 13.1 Spam / 139 13.2 Personalization / 142 13.3 Clustering / 142 13.4 Intelligent Agents / 143 13.5 Trends and Time-Sensitive Search / 144 13.6 Privacy and Censorship / 146 13.7 Library Classification Schemes / 147 13.8 Data Fusion / 148 14. Resources for Web Information Retrieval / 149 14.1 Resources for Getting Started / 149 14.2 Resources for Serious Study / 150 15. The Mathematics Guide / 153 15.1 Linear Algebra / 153 15.2 Perron--Frobenius Theory / 167 15.3 Markov Chains / 175 15.4 Perron Complementation / 186 15.5 Stochastic Complementation / 192 15.6 Censoring / 194 15.7 Aggregation / 195 15.8 Disaggregation / 198 16. Glossary / 201 Bibliography / 207 Index / 219

shortTOC:
Preface / ix 1: Introduction to Web Search Engines / 1 2: Crawling, Indexing, and Query Processing / 15 3: Ranking Webpages by Popularity / 25 4: The Mathematics of Google's PageRank / 31 5: Parameters in the PageRank Model / 47 6: The Sensitivity of PageRank / 57 7: The PageRank Problem as a Linear System / 71 8: Issues in Large-Scale Implementation of PageRank / 75 9: Accelerating the Computation of PageRank / 89 10: Updating the PageRank Vector / 99 11: The HITS Method for Ranking Webpages / 115 12: Other Link Methods for Ranking Webpages / 121 13: The Future of Web Information Retrieval / 139 14: Resources for Web Information Retrieval / 149 15: The Mathematics Guide / 153 16: Glossary / 201 Bibliography / 207 Index / 219

rawshortTOC:
Preface / ix \\ 1: Introduction to Web Search Engines / 1 \\ 2: Crawling, Indexing, and Query Processing / 15 \\ 3: Ranking Webpages by Popularity / 25 \\ 4: The Mathematics of Google's PageRank / 31 \\ 5: Parameters in the PageRank Model / 47 \\ 6: The Sensitivity of PageRank / 57 \\ 7: The PageRank Problem as a Linear System / 71 \\ 8: Issues in Large-Scale Implementation of PageRank / 75 \\ 9: Accelerating the Computation of PageRank / 89 \\ 10: Updating the PageRank Vector / 99 \\ 11: The HITS Method for Ranking Webpages / 115 \\ 12: Other Link Methods for Ranking Webpages / 121 \\ 13: The Future of Web Information Retrieval / 139 \\ 14: Resources for Web Information Retrieval / 149 \\ 15: The Mathematics Guide / 153 \\ 16: Glossary / 201 \\ Bibliography / 207 \\ Index / 219

rawTOC:
Preface / ix \\ 1. Introduction to Web Search Engines / 1 \\ 1.1 A Short History of Information Retrieval / 1 \\ 1.2 An Overview of Traditional Information Retrieval / 5 \\ 1.3 Web Information Retrieval / 9 \\ \\ 2. Crawling, Indexing, and Query Processing / 15 \\ 2.1 Crawling / 15 \\ 2.2 The Content Index / 19 \\ 2.3 Query Processing / 21 \\ \\ 3. Ranking Webpages by Popularity / 25 \\ 3.1 The Scene in 1998 / 25 \\ 3.2 Two Theses / 26 \\ 3.3 Query-Independence / 30 \\ \\ 4. The Mathematics of Google's PageRank / 31 \\ 4.1 The Original Summation Formula for PageRank / 32 \\ 4.2 Matrix Representation of the Summation Equations / 33 \\ 4.3 Problems with the Iterative Process / 34 \\ 4.4 A Little Markov Chain Theory / 36 \\ 4.5 Early Adjustments to the Basic Model / 36 \\ 4.6 Computation of the PageRank Vector / 39 \\ 4.7 Theorem and Proof for Spectrum of the Google Matrix / 45 \\ \\ 5. Parameters in the PageRank Model / 47 \\ 5.1 The $\alpha$ Factor / 47 \\ 5.2 The Hyperlink Matrix $H$ / 48 \\ 5.3 The Teleportation Matrix $E$ / 49 \\ \\ 6. The Sensitivity of PageRank / 57 \\ 6.1 Sensitivity with respect to $\alpha$ / 57 \\ 6.2 Sensitivity with respect to $H$ / 62 \\ 6.3 Sensitivity with respect to $v^T$ / 63 \\ 6.4 Other Analyses of Sensitivity / 63 \\ 6.5 Sensitivity Theorems and Proofs / 66 \\ \\ 7. The PageRank Problem as a Linear System / 71 \\ 7.1 Properties of $(I - $\alpha$ S)$ / 71 \\ 7.2 Properties of $(I - $\alpha$ H)$ / 72 \\ 7.3 Proof of the PageRank Sparse Linear System / 73 \\ \\ 8. Issues in Large-Scale Implementation of PageRank / 75 \\ 8.1 Storage Issues / 75 \\ 8.2 Convergence Criterion / 79 \\ 8.3 Accuracy / 79 \\ 8.4 Dangling Nodes / 80 \\ 8.5 Back Button Modeling / 84 \\ \\ 9. Accelerating the Computation of PageRank / 89 \\ 9.1 An Adaptive Power Method / 89 \\ 9.2 Extrapolation / 90 \\ 9.3 Aggregation / 94 \\ 9.4 Other Numerical Methods / 97 \\ \\ 10. Updating the PageRank Vector / 99 \\ 10.1 The Two Updating Problems and their History / 100 \\ 10.2 Restarting the Power Method / 101 \\ 10.3 Approximate Updating Using Approximate Aggregation / 102 \\ 10.4 Exact Aggregation / 104 \\ 10.5 Exact vs. Approximate Aggregation / 105 \\ 10.6 Updating with Iterative Aggregation / 107 \\ 10.7 Determining the Partition / 109 \\ 10.8 Conclusions / 111 \\ \\ 11. The HITS Method for Ranking Webpages / 115 \\ 11.1 The HITS Algorithm / 115 \\ 11.2 HITS Implementation / 117 \\ 11.3 HITS Convergence / 119 \\ 11.4 HITS Example / 120 \\ 11.5 Strengths and Weaknesses of HITS / 122 \\ 11.6 HITS's Relationship to Bibliometrics / 123 \\ 11.7 Query-Independent HITS / 124 \\ 11.8 Accelerating HITS / 126 \\ 11.9 HITS Sensitivity / 126 \\ \\ 12. Other Link Methods for Ranking Webpages / 131 \\ 12.1 SALSA / 131 \\ 12.2 Hybrid Ranking Methods / 135 \\ 12.3 Rankings based on Traffic Flow / 136 \\ \\ 13. The Future of Web Information Retrieval / 139 \\ 13.1 Spam / 139 \\ 13.2 Personalization / 142 \\ 13.3 Clustering / 142 \\ 13.4 Intelligent Agents / 143 \\ 13.5 Trends and Time-Sensitive Search / 144 \\ 13.6 Privacy and Censorship / 146 \\ 13.7 Library Classification Schemes / 147 \\ 13.8 Data Fusion / 148 \\ \\ 14. Resources for Web Information Retrieval / 149 \\ 14.1 Resources for Getting Started / 149 \\ 14.2 Resources for Serious Study / 150 \\ \\ 15. The Mathematics Guide / 153 \\ 15.1 Linear Algebra / 153 \\ 15.2 Perron--Frobenius Theory / 167 \\ 15.3 Markov Chains / 175 \\ 15.4 Perron Complementation / 186 \\ 15.5 Stochastic Complementation / 192 \\ 15.6 Censoring / 194 \\ 15.7 Aggregation / 195 \\ 15.8 Disaggregation / 198 \\ \\ 16. Glossary / 201 \\ \\ Bibliography / 207 \\ \\ Index / 219

entry:
@Book{Langville:2006:GPB,
  author =       "Amy N. Langville and Carl D. (Carl Dean) Meyer",
  title =        "{Google}'s {PageRank} and beyond: the science of
                 search engine rankings",
  publisher =    pub-PRINCETON,
  address =      pub-PRINCETON:adr,
  pages =        "x + 224",
  year =         "2006",
  ISBN =         "0-691-12202-4 (hardcover)",
  ISBN-13 =      "978-0-691-12202-1 (hardcover)",
  LCCN =         "TK5105.885.G66 L36 2006",
  MRclass =      "68-02 (00-01 00A05 15A18 68U35)",
  MRnumber =     "MR2262054 (2007h:68002)",
  MRreviewer =   "Jiu Ding",
  bibdate =      "Fri Oct 23 16:04:57 MDT 2009",
  bibsource =    "https://www.math.utah.edu/pub/bibnet/authors/g/golub-gene-h.bib;
                 https://www.math.utah.edu/pub/tex/bib/master.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.loc.gov:7090/Voyager",
  URL =          "http://www.loc.gov/catdir/enhancements/fy0654/2005938841-b.html;
                 http://www.loc.gov/catdir/enhancements/fy0654/2005938841-d.html;
                 http://www.loc.gov/catdir/enhancements/fy0668/2005938841-t.html",
  ZMnumber =     "1104.68042",
  acknowledgement = ack-nhfb,
  shorttableofcontents = "Preface / ix \\
                 1: Introduction to Web Search Engines / 1 \\
                 2: Crawling, Indexing, and Query Processing / 15 \\
                 3: Ranking Webpages by Popularity / 25 \\
                 4: The Mathematics of Google's PageRank / 31 \\
                 5: Parameters in the PageRank Model / 47 \\
                 6: The Sensitivity of PageRank / 57 \\
                 7: The PageRank Problem as a Linear System / 71 \\
                 8: Issues in Large-Scale Implementation of PageRank /
                 75 \\
                 9: Accelerating the Computation of PageRank / 89 \\
                 10: Updating the PageRank Vector / 99 \\
                 11: The HITS Method for Ranking Webpages / 115 \\
                 12: Other Link Methods for Ranking Webpages / 121 \\
                 13: The Future of Web Information Retrieval / 139 \\
                 14: Resources for Web Information Retrieval / 149 \\
                 15: The Mathematics Guide / 153 \\
                 16: Glossary / 201 \\
                 Bibliography / 207 \\
                 Index / 219",
  subject =      "Google; Web search engines; Web sites; Ratings;
                 Mathematics; Internet searching; World Wide Web;
                 Subject access",
  tableofcontents = "Preface / ix \\
                 1. Introduction to Web Search Engines / 1 \\
                 1.1 A Short History of Information Retrieval / 1 \\
                 1.2 An Overview of Traditional Information Retrieval /
                 5 \\
                 1.3 Web Information Retrieval / 9 \\
                 \\
                 2. Crawling, Indexing, and Query Processing / 15 \\
                 2.1 Crawling / 15 \\
                 2.2 The Content Index / 19 \\
                 2.3 Query Processing / 21 \\
                 \\
                 3. Ranking Webpages by Popularity / 25 \\
                 3.1 The Scene in 1998 / 25 \\
                 3.2 Two Theses / 26 \\
                 3.3 Query-Independence / 30 \\
                 \\
                 4. The Mathematics of Google's PageRank / 31 \\
                 4.1 The Original Summation Formula for PageRank / 32
                 \\
                 4.2 Matrix Representation of the Summation Equations /
                 33 \\
                 4.3 Problems with the Iterative Process / 34 \\
                 4.4 A Little Markov Chain Theory / 36 \\
                 4.5 Early Adjustments to the Basic Model / 36 \\
                 4.6 Computation of the PageRank Vector / 39 \\
                 4.7 Theorem and Proof for Spectrum of the Google Matrix
                 / 45 \\
                 \\
                 5. Parameters in the PageRank Model / 47 \\
                 5.1 The $\alpha$ Factor / 47 \\
                 5.2 The Hyperlink Matrix $H$ / 48 \\
                 5.3 The Teleportation Matrix $E$ / 49 \\
                 \\
                 6. The Sensitivity of PageRank / 57 \\
                 6.1 Sensitivity with respect to $\alpha$ / 57 \\
                 6.2 Sensitivity with respect to $H$ / 62 \\
                 6.3 Sensitivity with respect to $v^T$ / 63 \\
                 6.4 Other Analyses of Sensitivity / 63 \\
                 6.5 Sensitivity Theorems and Proofs / 66 \\
                 \\
                 7. The PageRank Problem as a Linear System / 71 \\
                 7.1 Properties of $(I - $\alpha$ S)$ / 71 \\
                 7.2 Properties of $(I - $\alpha$ H)$ / 72 \\
                 7.3 Proof of the PageRank Sparse Linear System / 73 \\
                 \\
                 8. Issues in Large-Scale Implementation of PageRank /
                 75 \\
                 8.1 Storage Issues / 75 \\
                 8.2 Convergence Criterion / 79 \\
                 8.3 Accuracy / 79 \\
                 8.4 Dangling Nodes / 80 \\
                 8.5 Back Button Modeling / 84 \\
                 \\
                 9. Accelerating the Computation of PageRank / 89 \\
                 9.1 An Adaptive Power Method / 89 \\
                 9.2 Extrapolation / 90 \\
                 9.3 Aggregation / 94 \\
                 9.4 Other Numerical Methods / 97 \\
                 \\
                 10. Updating the PageRank Vector / 99 \\
                 10.1 The Two Updating Problems and their History / 100
                 \\
                 10.2 Restarting the Power Method / 101 \\
                 10.3 Approximate Updating Using Approximate Aggregation
                 / 102 \\
                 10.4 Exact Aggregation / 104 \\
                 10.5 Exact vs. Approximate Aggregation / 105 \\
                 10.6 Updating with Iterative Aggregation / 107 \\
                 10.7 Determining the Partition / 109 \\
                 10.8 Conclusions / 111 \\
                 \\
                 11. The HITS Method for Ranking Webpages / 115 \\
                 11.1 The HITS Algorithm / 115 \\
                 11.2 HITS Implementation / 117 \\
                 11.3 HITS Convergence / 119 \\
                 11.4 HITS Example / 120 \\
                 11.5 Strengths and Weaknesses of HITS / 122 \\
                 11.6 HITS's Relationship to Bibliometrics / 123 \\
                 11.7 Query-Independent HITS / 124 \\
                 11.8 Accelerating HITS / 126 \\
                 11.9 HITS Sensitivity / 126 \\
                 \\
                 12. Other Link Methods for Ranking Webpages / 131 \\
                 12.1 SALSA / 131 \\
                 12.2 Hybrid Ranking Methods / 135 \\
                 12.3 Rankings based on Traffic Flow / 136 \\
                 \\
                 13. The Future of Web Information Retrieval / 139 \\
                 13.1 Spam / 139 \\
                 13.2 Personalization / 142 \\
                 13.3 Clustering / 142 \\
                 13.4 Intelligent Agents / 143 \\
                 13.5 Trends and Time-Sensitive Search / 144 \\
                 13.6 Privacy and Censorship / 146 \\
                 13.7 Library Classification Schemes / 147 \\
                 13.8 Data Fusion / 148 \\
                 \\
                 14. Resources for Web Information Retrieval / 149 \\
                 14.1 Resources for Getting Started / 149 \\
                 14.2 Resources for Serious Study / 150 \\
                 \\
                 15. The Mathematics Guide / 153 \\
                 15.1 Linear Algebra / 153 \\
                 15.2 Perron--Frobenius Theory / 167 \\
                 15.3 Markov Chains / 175 \\
                 15.4 Perron Complementation / 186 \\
                 15.5 Stochastic Complementation / 192 \\
                 15.6 Censoring / 194 \\
                 15.7 Aggregation / 195 \\
                 15.8 Disaggregation / 198 \\
                 \\
                 16. Glossary / 201 \\
                 \\
                 Bibliography / 207 \\
                 \\
                 Index / 219",
}


bibtype: article
filename: pagerank.bib
label: Langville:2006:UMC
author: Amy N. Langville and Carl D. Meyer
title: Updating Markov chains with an eye on Google's PageRank
journal: j-SIAM-J-MAT-ANA-APPL
volume: 27
number: 4
pages: 968--987
year: 2006
CODEN: SJMAEL
DOI: https://doi.org/10.1137/040619028
ISSN: 0895-4798 (print), 1095-7162 (electronic) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
MRnumber: MR2205607
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
fjournal: SIAM Journal on Matrix Analysis and Applications
ZMnumber: 1098.60073
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
rawauthor: Amy N. Langville and Carl D. Meyer
rawtitle: Updating {Markov} chains with an eye on {Google}'s {PageRank}
MRclass:
60J10 (65C40 68P20 68U35); 60J10 65C40 15A51 65F10 65F15 65F30 65F50 68P20 68P10 15A99 15-04 15A18 15A06

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


bibtype: inproceedings
filename: pagerank.bib
label: Lin:2006:PNL
author: Zhenjiang Lin and I. King and M. R. Lyu
title: PageSim: a Novel Link-Based Similarity Measure for the World Wide Web
crossref: Nishida:2006:IWA
pages: 687--693
year: 2006
DOI: https://doi.org/10.1109/WI.2006.127
bibdate: Thu May 06 16:01:07 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:01:07 ???
acknowledgement: ack-nhfb
rawauthor: Zhenjiang Lin and I. King and M. R. Lyu
rawtitle: {PageSim}: a Novel Link-Based Similarity Measure for the {World Wide Web}
entry:
@InProceedings{Lin:2006:PNL,
  author =       "Zhenjiang Lin and I. King and M. R. Lyu",
  title =        "{PageSim}: a Novel Link-Based Similarity Measure for
                 the {World Wide Web}",
  crossref =     "Nishida:2006:IWA",
  pages =        "687--693",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/WI.2006.127",
  bibdate =      "Thu May 06 16:01:07 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: incollection
filename: pagerank.bib
label: MadriddelaVega:2006:NLA
author: Humberto Madrid de la Vega and Valia Guerra Ones and Marisol Flores Garrido
booktitle: Papers of the Mexican Mathematical Society (Spanish)
title: The numerical linear algebra of Google's PageRank
volume: 36
publisher: Soc. Mat. Mexicana
address: Mexico
pages: 33--52
year: 2006
MRclass: 65F15; 68P10 68M10 65F10 65F15 65F30 65F50
MRnumber: MR2347016 (2008j:65059)
MRreviewer: Juan R. Torregrosa
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
series: Aportaciones Mat. Comun.
ZMnumber: 1119.68357
acknowledgement: ack-nhfb
rawauthor: Humberto {Madrid de la Vega} and Valia Guerra Ones and Marisol Flores Garrido
rawbooktitle: {Papers of the Mexican Mathematical Society (Spanish)}
rawtitle: The numerical linear algebra of {Google}'s {PageRank}
entry:
@InCollection{MadriddelaVega:2006:NLA,
  author =       "Humberto {Madrid de la Vega} and Valia Guerra Ones and
                 Marisol Flores Garrido",
  booktitle =    "{Papers of the Mexican Mathematical Society
                 (Spanish)}",
  title =        "The numerical linear algebra of {Google}'s
                 {PageRank}",
  volume =       "36",
  publisher =    "Soc. Mat. Mexicana",
  address =      "M\'exico",
  pages =        "33--52",
  year =         "2006",
  ISBN =         "????",
  ISBN-13 =      "????",
  MRclass =      "65F15; 68P10 68M10 65F10 65F15 65F30 65F50",
  MRnumber =     "MR2347016 (2008j:65059)",
  MRreviewer =   "Juan R. Torregrosa",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Aportaciones Mat. Comun.",
  ZMnumber =     "1119.68357",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Murata:2006:EKW
author: T. Murata and K. Saito
title: Extracting Keywords of Web Users' Interests and Visualizing their Routine Visits
crossref: IEEE:2006:ICC
pages: 1--66
year: 2006
DOI: https://doi.org/10.1109/ICARCV.2006.345367
bibdate: Thu May 06 15:04:16 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:04:16 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: T. Murata and K. Saito
rawtitle: Extracting Keywords of {Web} Users' Interests and Visualizing their Routine Visits
entry:
@InProceedings{Murata:2006:EKW,
  author =       "T. Murata and K. Saito",
  title =        "Extracting Keywords of {Web} Users' Interests and
                 Visualizing their Routine Visits",
  crossref =     "IEEE:2006:ICC",
  pages =        "1--66",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/ICARCV.2006.345367",
  bibdate =      "Thu May 06 15:04:16 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: Neate:2006:CNF
author: B. Neate and W. Irwin and N. Churcher
title: CodeRank: a new family of software metrics
crossref: IEEE:2006:ASE
pages: 369--378 (check??)
year: 2006
bibdate: Thu May 06 15:14:28 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:14:28 ???
acknowledgement: ack-nhfb
rawauthor: B. Neate and W. Irwin and N. Churcher
rawtitle: {CodeRank}: a new family of software metrics
entry:
@InProceedings{Neate:2006:CNF,
  author =       "B. Neate and W. Irwin and N. Churcher",
  title =        "{CodeRank}: a new family of software metrics",
  crossref =     "IEEE:2006:ASE",
  pages =        "369--378 (check??)",
  year =         "2006",
  bibdate =      "Thu May 06 15:14:28 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Ono:2006:IWS
author: H. Ono and M. Toyoda and M. Kitsuregawa
title: Identifying Web Spam by Densely Connected Sites and its Statistics in a Japanese Web Snapshot
crossref: Barga:2006:IPI
pages: x131--x131
year: 2006
DOI: https://doi.org/10.1109/ICDEW.2006.64
bibdate: Thu May 06 17:00:09 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 17:00:09 ???
acknowledgement: ack-nhfb
rawauthor: H. Ono and M. Toyoda and M. Kitsuregawa
rawtitle: Identifying {Web} Spam by Densely Connected Sites and its Statistics in a {Japanese Web} Snapshot
entry:
@InProceedings{Ono:2006:IWS,
  author =       "H. Ono and M. Toyoda and M. Kitsuregawa",
  title =        "Identifying {Web} Spam by Densely Connected Sites and
                 its Statistics in a {Japanese Web} Snapshot",
  crossref =     "Barga:2006:IPI",
  pages =        "x131--x131",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/ICDEW.2006.64",
  bibdate =      "Thu May 06 17:00:09 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: Pandurangan:2006:UPC
author: Gopal Pandurangan and Prabhakar Raghavan and Eli Upfal
title: Using PageRank to characterize web structure
journal: j-INTERNET-MATH
volume: 3
number: 1
pages: 1--20
year: 2006
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 68U35 (05C07 05C80 68M10); 68M10 68P10 68W05
MRnumber: MR2283881 (2007k:68135)
MRreviewer: Mirel Co{\c{s}}ulschi
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1175266365
fjournal: Internet Mathematics
ZMnumber: 1113.68313
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Gopal Pandurangan and Prabhakar Raghavan and Eli Upfal
rawtitle: Using {PageRank} to characterize web structure
entry:
@Article{Pandurangan:2006:UPC,
  author =       "Gopal Pandurangan and Prabhakar Raghavan and Eli
                 Upfal",
  title =        "Using {PageRank} to characterize web structure",
  journal =      j-INTERNET-MATH,
  volume =       "3",
  number =       "1",
  pages =        "1--20",
  year =         "2006",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "68U35 (05C07 05C80 68M10); 68M10 68P10 68W05",
  MRnumber =     "MR2283881 (2007k:68135)",
  MRreviewer =   "Mirel Co{\c{s}}ulschi",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1175266365",
  ZMnumber =     "1113.68313",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: inproceedings
filename: pagerank.bib
label: Parreira:2006:EDP
author: Josiane Xavier Parreira and Debora Donato and Sebastian Michel and Gerhard Weikum
editor: Umeshwar Dayal and others
booktitle: Proceedings of the 32nd International Conference on Very Large Data Bases
title: Efficient and decentralized PageRank approximation in a peer-to-peer Web search network
publisher: pub-ACM
address: pub-ACM:adr
pages: 415--426
year: 2006
DOI: https://doi.org/10.1109/ICDCS.2005.84
ISBN: 1-59593-385-9 OR 1595933859
ISBN13: 978-1-59593-385-0 OR 9781595933850
LCCN: QA76.9.D3 I61 2006
bibdate: Sat May 8 18:33:11 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:11 MDT
acknowledgement: ack-nhfb
bookpages: xxxi + 1269 (two volumes)
rawauthor: Josiane Xavier Parreira and Debora Donato and Sebastian Michel and Gerhard Weikum
rawbooktitle: Proceedings of the 32nd International Conference on Very Large Data Bases
raweditor: Umeshwar Dayal and others
rawtitle: Efficient and decentralized {PageRank} approximation in a peer-to-peer {Web} search network
abstract:
PageRank-style (PR) link analyses are a cornerstone of Web search engines and Web mining, but they are computationally expensive. Recently, various techniques have been proposed for speeding up these analyses by distributing the link graph among multiple sites. However, none of these advanced methods is suitable for a fully decentralized PR computation in a peer-to-peer (P2P) network with autonomous peers, where each peer can independently crawl Web fragments according to the user's thematic interests. In such a setting the graph fragments that different peers have locally available or know about may arbitrarily overlap among peers, creating additional complexity for the PR computation.This paper presents the JXP algorithm for dynamically and collaboratively computing PR scores of Web pages that are arbitrarily distributed in a P2P network. The algorithm runs at every peer, and it works by combining locally computed PR scores with random meetings among the peers in the network. It is scalable as the number of peers on the network grows, and experiments as well as theoretical arguments show that JXP scores converge to the true PR scores that one would obtain by a centralized computation.

rawabstract:
PageRank-style (PR) link analyses are a cornerstone of Web search engines and Web mining, but they are computationally expensive. Recently, various techniques have been proposed for speeding up these analyses by distributing the link graph among multiple sites. However, none of these advanced methods is suitable for a fully decentralized PR computation in a peer-to-peer (P2P) network with autonomous peers, where each peer can independently crawl Web fragments according to the user's thematic interests. In such a setting the graph fragments that different peers have locally available or know about may arbitrarily overlap among peers, creating additional complexity for the PR computation.This paper presents the JXP algorithm for dynamically and collaboratively computing PR scores of Web pages that are arbitrarily distributed in a P2P network. The algorithm runs at every peer, and it works by combining locally computed PR scores with random meetings among the peers in the network. It is scalable as the number of peers on the network grows, and experiments as well as theoretical arguments show that JXP scores converge to the true PR scores that one would obtain by a centralized computation.

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


bibtype: inproceedings
filename: pagerank.bib
label: Peng:2006:RWS
author: Wen-Chih Peng and Yu-Chin Lin
title: Ranking Web Search Results from Personalized Perspective
crossref: Wombacher:2006:JCC
pages: 12--12
year: 2006
DOI: https://doi.org/10.1109/CEC-EEE.2006.72
bibdate: Thu May 06 15:52:27 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:52:27 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: Wen-Chih Peng and Yu-Chin Lin
rawtitle: Ranking {Web} Search Results from Personalized Perspective
entry:
@InProceedings{Peng:2006:RWS,
  author =       "Wen-Chih Peng and Yu-Chin Lin",
  title =        "Ranking {Web} Search Results from Personalized
                 Perspective",
  crossref =     "Wombacher:2006:JCC",
  pages =        "12--12",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/CEC-EEE.2006.72",
  bibdate =      "Thu May 06 15:52:27 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: Quesada:2006:HIP
author: A. Arratia Quesada and C. Marijuan
booktitle: Fifth Conference on Discrete Mathematics and Computer Science (Spanish)
title: How to improve the PageRank of a tree
volume: 23
publisher: Universidad Valladolid
address: Secr. Publ. Intercamb. Ed., Valladolid, Spain
pages: 71--78
year: 2006
MRclass: 05C80 (68U35)
MRnumber: MR2325945
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
series: Ciencias (Valladolid)
ZMnumber: 05555980
acknowledgement: ack-nhfb
rawauthor: A. Arratia Quesada and C. Mariju{\'a}n
rawbooktitle: {Fifth Conference on Discrete Mathematics and Computer Science (Spanish)}
rawtitle: How to improve the {PageRank} of a tree
entry:
@InProceedings{Quesada:2006:HIP,
  author =       "A. Arratia Quesada and C. Mariju{\'a}n",
  booktitle =    "{Fifth Conference on Discrete Mathematics and Computer
                 Science (Spanish)}",
  title =        "How to improve the {PageRank} of a tree",
  volume =       "23",
  publisher =    "Universidad Valladolid",
  address =      "Secr. Publ. Intercamb. Ed., Valladolid, Spain",
  pages =        "71--78",
  year =         "2006",
  ISBN =         "????",
  ISBN-13 =      "????",
  MRclass =      "05C80 (68U35)",
  MRnumber =     "MR2325945",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Ciencias (Valladolid)",
  ZMnumber =     "05555980",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Radev:2006:GBM
author: D. R. Radev
title: Graph-Based Methods for Language Processing and Information Retrieval
crossref: IEEE:2006:ISL
pages: 4--4
year: 2006
DOI: https://doi.org/10.1109/SLT.2006.326781
bibdate: Thu May 06 16:42:55 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:42:55 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: D. R. Radev
rawtitle: Graph-Based Methods for Language Processing and Information Retrieval
entry:
@InProceedings{Radev:2006:GBM,
  author =       "D. R. Radev",
  title =        "Graph-Based Methods for Language Processing and
                 Information Retrieval",
  crossref =     "IEEE:2006:ISL",
  pages =        "4--4",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/SLT.2006.326781",
  bibdate =      "Thu May 06 16:42:55 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: Richardson:2006:BPM
author: Matthew Richardson and Amit Prakash and Eric Brill
editor: ACM
title: Beyond PageRank: machine learning for static ranking
publisher: pub-ACM
address: pub-ACM:adr
pages: 707--715
year: 2006
DOI: https://doi.org/10.1145/858476.858479
ISBN: 1-59593-323-9 OR 1595933239
ISBN13: 978-1-59593-323-2 OR 9781595933232
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
keywords: PageRank; RankNet; relevance; search engines; static ranking
acknowledgement: ack-nhfb
rawauthor: Matthew Richardson and Amit Prakash and Eric Brill
raweditor: {ACM}
rawtitle: Beyond {PageRank}: machine learning for static ranking
booktitle:
International World Wide Web Conference Proceedings of the 15th international conference on World Wide Web

abstract:
Since the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-independent) ordering of Web pages. We show that we can significantly outperform PageRank using features that are independent of the link structure of the Web. We gain a further boost in accuracy by using data on the frequency at which users visit Web pages. We use RankNet, a ranking machine learning algorithm, to combine these and other static features based on anchor text and domain characteristics. The resulting model achieves a static ranking pairwise accuracy of 67.3% (vs. 56.7% for PageRank or 50% for random).

rawabstract:
Since the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-independent) ordering of Web pages. We show that we can significantly outperform PageRank using features that are independent of the link structure of the Web. We gain a further boost in accuracy by using data on the frequency at which users visit Web pages. We use RankNet, a ranking machine learning algorithm, to combine these and other static features based on anchor text and domain characteristics. The resulting model achieves a static ranking pairwise accuracy of 67.3\% (vs. 56.7\% for PageRank or 50\% for random).

rawbooktitle:
{International World Wide Web Conference Proceedings of the 15th international conference on World Wide Web}

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


bibtype: inproceedings
filename: pagerank.bib
label: Rungsawang:2006:PAT
author: Arnon Rungsawang and Bundit Manaskasemsak
title: Parallel adaptive technique for computing PageRank
crossref: IEEE:2005:EIC
pages: 15--50
year: 2006
DOI: https://doi.org/10.1109/PDP.2006.55
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=1613249
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10741
rawauthor: Arnon Rungsawang and Bundit Manaskasemsak
rawtitle: Parallel adaptive technique for computing {PageRank}
booktitle:
PDP 2006: 14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

abstract:
Re-ranking the search results using PageRank is a well-known technique used in modern search engines. Running an iterative algorithm like PageRank on a large web graph consumes both much computing resource and time. This paper therefore proposes a parallel adaptive technique for computing PageRank using the PC cluster. Following the study of the Stanford WebBase group on convergence patterns of PageRank scores of pages using the conventional PageRank algorithm, PageRank scores of most pages converge more quickly than the remainder, we then devise our parallel adaptive algorithm to reiterate the computation for pages whose PageRank scores are still not converged. From experiments using a synthesized web graph of 28 million pages and around 227 million hyperlinks, we obtain the acceleration rate up to 6-8 times using 32 PC processors.

rawabstract:
Re-ranking the search results using PageRank is a well-known technique used in modern search engines. Running an iterative algorithm like PageRank on a large web graph consumes both much computing resource and time. This paper therefore proposes a parallel adaptive technique for computing PageRank using the PC cluster. Following the study of the Stanford WebBase group on convergence patterns of PageRank scores of pages using the conventional PageRank algorithm, PageRank scores of most pages converge more quickly than the remainder, we then devise our parallel adaptive algorithm to reiterate the computation for pages whose PageRank scores are still not converged. From experiments using a synthesized web graph of 28 million pages and around 227 million hyperlinks, we obtain the acceleration rate up to 6-8 times using 32 PC processors.

rawbooktitle:
{PDP 2006: 14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing}

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


bibtype: inproceedings
filename: pagerank.bib
label: Sarlos:2006:RRS
author: Tamas Sarlos and Adras A. Benczur and Karoly Csalogany and Daniel Fogaras and Balazs Racz
editor: ACM
booktitle: Proceedings of the 15th international conference on World Wide Web, Edinburgh, Scotland
title: To randomize or not to randomize: space optimal summaries for hyperlink analysis
publisher: pub-ACM
address: pub-ACM:adr
pages: 297--306
year: 2006
DOI: https://doi.org/10.1145/1135777.1135823
ISBN: 1-59593-323-9 OR 1595933239
ISBN13: 978-1-59593-323-2 OR 9781595933232
bibdate: Mon May 10 13:56:03 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.10 13:56:03 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawbooktitle: {Proceedings of the 15th international conference on World Wide Web, Edinburgh, Scotland}
raweditor: ACM
rawtitle: To randomize or not to randomize: space optimal summaries for hyperlink analysis
abstract:
Personalized PageRank expresses link-based page quality around user selected pages. The only previous personalized PageRank algorithm that can serve on-line queries for an unrestricted choice of pages on large graphs is our Monte Carlo algorithm [WAW 2004]. In this paper we achieve unrestricted personalization by combining rounding and randomized sketching techniques in the dynamic programming algorithm of Jeh and Widom [WWW 2003]. We evaluate the precision of approximation experimentally on large scale real-world data and find significant improvement over previous results. As a key theoretical contribution we show that our algorithms use an optimal amount of space by also improving earlier asymptotic worst-case lower bounds. Our lower bounds and algorithms apply to the SimRank as well; of independent interest is the reduction of the SimRank computation to personalized PageRank.

rawabstract:
Personalized PageRank expresses link-based page quality around user selected pages. The only previous personalized PageRank algorithm that can serve on-line queries for an unrestricted choice of pages on large graphs is our Monte Carlo algorithm [WAW 2004]. In this paper we achieve unrestricted personalization by combining rounding and randomized sketching techniques in the dynamic programming algorithm of Jeh and Widom [WWW 2003]. We evaluate the precision of approximation experimentally on large scale real-world data and find significant improvement over previous results. As a key theoretical contribution we show that our algorithms use an optimal amount of space by also improving earlier asymptotic worst-case lower bounds. Our lower bounds and algorithms apply to the SimRank as well; of independent interest is the reduction of the SimRank computation to personalized PageRank.

rawauthor:
Tam{\'a}s Sarl{\'o}s and Adr{\'a}s A. Bencz{\'u}r and K{\'a}roly Csalog{\'a}ny and D{\'a}niel Fogaras and Bal{\'a}zs R{\'a}cz

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


bibtype: article
filename: pagerank.bib
label: Sun:2006:NPA
author: Huan Sun and Yimin Wei
title: A note on the PageRank algorithm
journal: j-APPL-MATH-COMP
volume: 179
number: 2
pages: 799--806
day: 15
month: aug
monthnumber: 8
year: 2006
CODEN: AMHCBQ
DOI: https://doi.org/10.1016/j.amc.2005.11.120
ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
MRclass: 65F15
MRnumber: MR2293192
bibdate: Sat Jul 12 09:02:57 MDT 2008
bibtimestamp: 2008.07.12 09:02:57 MDT
URL: https://www.math.utah.edu/pub/tex/bib/applmathcomput2005.bib
fjournal: Applied Mathematics and Computation
ZMnumber: 1103.68973
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00963003
rawauthor: Huan Sun and Yimin Wei
rawtitle: A note on the {PageRank} algorithm
bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; http://www.sciencedirect.com/science/journal/00963003

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


bibtype: inproceedings
filename: pagerank.bib
label: Tong:2006:FRW
author: Hanghang Tong and C. Faloutsos and J.-Y. Pan
title: Fast Random Walk with Restart and Its Applications
crossref: Clifton:2006:SIC
pages: 613--622
year: 2006
DOI: https://doi.org/10.1109/ICDM.2006.70
bibdate: Thu May 06 16:55:53 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:55:53 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: Hanghang Tong and C. Faloutsos and J.-Y. Pan
rawtitle: Fast Random Walk with Restart and Its Applications
entry:
@InProceedings{Tong:2006:FRW,
  author =       "Hanghang Tong and C. Faloutsos and J.-Y. Pan",
  title =        "Fast Random Walk with Restart and Its Applications",
  crossref =     "Clifton:2006:SIC",
  pages =        "613--622",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/ICDM.2006.70",
  bibdate =      "Thu May 06 16:55:53 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
  xxcrossref =   "Perner:2006:ADM",
}


bibtype: article
filename: pagerank.bib
label: Wills:2006:GPM
author: Rebecca S. Wills
title: Google's PageRank: the math behind the search engine
journal: j-MATH-INTEL
volume: 28
number: 4
pages: 6--11
year: 2006
CODEN: MAINDC
DOI: https://doi.org/10.1007/BF02984696
ISSN: 0343-6993 (print), 1866-7414 (electronic) OR 03436993 (print), 18667414 (electronic)
ISSNL: 0343-6993 OR 03436993
MRclass: 05C80 (00A99 15A18)
MRnumber: MR2272767
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
fjournal: The Mathematical Intelligencer
acknowledgement: ack-nhfb
rawauthor: Rebecca S. Wills
rawtitle: {Google}'s {PageRank}: the math behind the search engine
entry:
@Article{Wills:2006:GPM,
  author =       "Rebecca S. Wills",
  title =        "{Google}'s {PageRank}: the math behind the search
                 engine",
  journal =      j-MATH-INTEL,
  volume =       "28",
  number =       "4",
  pages =        "6--11",
  year =         "2006",
  CODEN =        "MAINDC",
  DOI =          "https://doi.org/10.1007/BF02984696",
  ISSN =         "0343-6993 (print), 1866-7414 (electronic)",
  ISSN-L =       "0343-6993",
  MRclass =      "05C80 (00A99 15A18)",
  MRnumber =     "MR2272767",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "The Mathematical Intelligencer",
}


bibtype: inproceedings
filename: pagerank.bib
label: Wissner-Gross:2006:PTR
author: A. D. Wissner-Gross
title: Preparation of Topical Reading Lists from the Link Structure of Wikipedia
crossref: IEEE:2006:SIC
pages: 825--829
year: 2006
DOI: https://doi.org/10.1109/ICALT.2006.1652568
bibdate: Thu May 06 16:05:06 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:05:06 ???
acknowledgement: ack-nhfb
rawauthor: A. D. Wissner-Gross
rawtitle: Preparation of Topical Reading Lists from the Link Structure of {Wikipedia}
entry:
@InProceedings{Wissner-Gross:2006:PTR,
  author =       "A. D. Wissner-Gross",
  title =        "Preparation of Topical Reading Lists from the Link
                 Structure of {Wikipedia}",
  crossref =     "IEEE:2006:SIC",
  pages =        "825--829",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/ICALT.2006.1652568",
  bibdate =      "Thu May 06 16:05:06 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Yang:2006:PRG
author: Haixuan Yang and Irwin King and M. R. Lyu
title: Predictive Random Graph Ranking on the Web
crossref: IEEE:2006:IJC
pages: 1825--1832
year: 2006
DOI: https://doi.org/10.1109/IJCNN.2006.246901
bibdate: Thu May 06 16:06:14 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:06:14 ???
acknowledgement: ack-nhfb
rawauthor: Haixuan Yang and Irwin King and M. R. Lyu
rawtitle: Predictive Random Graph Ranking on the {Web}
entry:
@InProceedings{Yang:2006:PRG,
  author =       "Haixuan Yang and Irwin King and M. R. Lyu",
  title =        "Predictive Random Graph Ranking on the {Web}",
  crossref =     "IEEE:2006:IJC",
  pages =        "1825--1832",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/IJCNN.2006.246901",
  bibdate =      "Thu May 06 16:06:14 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Zhang:2006:XLM
author: Yi Zhang and Lei Zhang and Yan Zhang and Xiaoming Li
title: XRank: Learning More from Web User Behaviors
crossref: Jeong:2006:SII
pages: 36--36
year: 2006
DOI: https://doi.org/10.1109/CIT.2006.198
bibdate: Thu May 06 16:14:21 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:14:21 ???
acknowledgement: ack-nhfb
rawauthor: Yi Zhang and Lei Zhang and Yan Zhang and Xiaoming Li
rawtitle: {XRank}: Learning More from {Web} User Behaviors
entry:
@InProceedings{Zhang:2006:XLM,
  author =       "Yi Zhang and Lei Zhang and Yan Zhang and Xiaoming Li",
  title =        "{XRank}: Learning More from {Web} User Behaviors",
  crossref =     "Jeong:2006:SII",
  pages =        "36--36",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/CIT.2006.198",
  bibdate =      "Thu May 06 16:14:21 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Zhuang:2006:ACM
author: Yueting Zhuang and Hanhuai Shan and Fei Wu
booktitle: Proceedings of the 2006 12th International Multi-Media Modelling Conference
title: An approach for cross-media retrieval with cross-reference graph and PageRank
crossref: Feng:2006:IMM
pages: ??--??
year: 2006
DOI: https://doi.org/10.1109/MMMC.2006.1651316
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=1651316
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10988
rawauthor: Yueting Zhuang and Hanhuai Shan and Fei Wu
rawbooktitle: {Proceedings of the 2006 12th International Multi-Media Modelling Conference}
rawtitle: An approach for cross-media retrieval with cross-reference graph and {PageRank}
entry:
@InProceedings{Zhuang:2006:ACM,
  author =       "Yueting Zhuang and Hanhuai Shan and Fei Wu",
  booktitle =    "{Proceedings of the 2006 12th International
                 Multi-Media Modelling Conference}",
  title =        "An approach for cross-media retrieval with
                 cross-reference graph and {PageRank}",
  crossref =     "Feng:2006:IMM",
  pages =        "??--??",
  year =         "2006",
  DOI =          "https://doi.org/10.1109/MMMC.2006.1651316",
  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=1651316",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10988",
  pagecount =    "8",
}


bibtype: inproceedings
filename: pagerank.bib
label: Al-Saffar:2007:EBU
author: Sinan Al-Saffar and Gregory Heileman
editor: IEEE
booktitle: IEEE / WIC / ACM International Conference on Web Intelligence
title: Experimental Bounds on the Usefulness of Personalized and Topic-Sensitive PageRank
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 671--675
year: 2007
DOI: https://doi.org/10.1109/WI.2007.75
ISBN: 0-7695-3026-5 OR 0769530265
ISBN13: 978-0-7695-3026-0 OR 9780769530260
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=4427171
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4427043
rawauthor: Sinan Al-Saffar and Gregory Heileman
rawbooktitle: {IEEE\slash WIC\slash ACM International Conference on Web Intelligence}
raweditor: {IEEE}
rawtitle: Experimental Bounds on the Usefulness of Personalized and Topic-Sensitive {PageRank}
abstract:
PageRank is an algorithm used by several search engines to rank web documents according to their assumed relevance and popularity deduced from the Web's link structure. PageRank determines a global ordering of candidate search results according to each page's popularity as determined by the number and importance of pages linking to these results. Personalized and topic-sensitive PageRank are variants of the algorithm that return a local ranking based on each user's preferences as biased by a set of pages they trust or topics they prefer. In this paper we compare personalized and topic-sensitive local PageRanks to the global PageRank showing experimentally how similar or dissimilar results of personalization can be to the original global rank results and to other personalizations. Our approach is to examine a snapshot of the Web and determine how advantageous personalization can be in the best and worst cases and how it performs at various values of the damping factor in the PageRank formula.

rawabstract:
PageRank is an algorithm used by several search engines to rank web documents according to their assumed relevance and popularity deduced from the Web's link structure. PageRank determines a global ordering of candidate search results according to each page's popularity as determined by the number and importance of pages linking to these results. Personalized and topic-sensitive PageRank are variants of the algorithm that return a local ranking based on each user's preferences as biased by a set of pages they trust or topics they prefer. In this paper we compare personalized and topic-sensitive local PageRanks to the global PageRank showing experimentally how similar or dissimilar results of personalization can be to the original global rank results and to other personalizations. Our approach is to examine a snapshot of the Web and determine how advantageous personalization can be in the best and worst cases and how it performs at various values of the damping factor in the PageRank formula.

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


bibtype: inproceedings
filename: pagerank.bib
label: Al-Saffar:2007:PTS
author: S. Al-Saffar and G. Heileman
title: Personalized and Topic-Sensitive PageRank
crossref: Lin:2007:PIW
pages: 671--675
year: 2007
DOI: https://doi.org/10.1109/WI.2007.75
bibdate: Fri Feb 19 15:48:36 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 15:48:36 ???
acknowledgement: ack-nhfb
rawauthor: S. Al-Saffar and G. Heileman
rawtitle: Personalized and Topic-Sensitive {PageRank}
entry:
@InProceedings{Al-Saffar:2007:PTS,
  author =       "S. Al-Saffar and G. Heileman",
  title =        "Personalized and Topic-Sensitive {PageRank}",
  crossref =     "Lin:2007:PIW",
  pages =        "671--675",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/WI.2007.75",
  bibdate =      "Fri Feb 19 15:48:36 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Andersen:2007:DSD
author: Reid Andersen and Fan Chung
title: Detecting sharp drops in PageRank and a simplified local partitioning algorithm
crossref: Cai:2007:TAM
pages: 1--12
year: 2007
DOI: https://doi.org/10.1007/978-3-540-72504-6_1
MRclass: 68M10 (68U35)
MRnumber: MR2374293 (2008m:68006)
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
ZMnumber: 05211353
acknowledgement: ack-nhfb
rawauthor: Reid Andersen and Fan Chung
rawtitle: Detecting sharp drops in {PageRank} and a simplified local partitioning algorithm
abstract:
We show that whenever there is a sharp drop in the numerical rank defined by a personalized PageRank vector, the location of the drop reveals a cut with small conductance. We then show that for any cut in the graph, and for many starting vertices within that cut, an approximate personalized PageRank vector will have a sharp drop sufficient to produce a cut with conductance nearly as small as the original cut. Using this technique, we produce a nearly linear time local partitioning algorithm whose analysis is simpler than previous algorithms.

rawabstract:
We show that whenever there is a sharp drop in the numerical rank defined by a personalized PageRank vector, the location of the drop reveals a cut with small conductance. We then show that for any cut in the graph, and for many starting vertices within that cut, an approximate personalized PageRank vector will have a sharp drop sufficient to produce a cut with conductance nearly as small as the original cut. Using this technique, we produce a nearly linear time local partitioning algorithm whose analysis is simpler than previous algorithms.

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


bibtype: inproceedings
filename: pagerank.bib
label: Andersen:2007:LCP
title: Local computation of PageRank contributions
crossref: Bonato:2007:AMW
pages: 150--165
year: 2007
DOI: https://doi.org/10.1007/978-3-540-77004-6_12
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
MRclass: 05C90 (68R10 68U35 68W25)
MRnumber: MR2504913 (2010f:05175)
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
series: ser-LNCS
acknowledgement: ack-nhfb
rawtitle: Local computation of {PageRank} contributions
author:
Reid Andersen and Christian Borgs and Jennifer Chayes and John Hopcraft and Vahab S. Mirrokni and Shang-Hua Teng

rawauthor:
Reid Andersen and Christian Borgs and Jennifer Chayes and John Hopcraft and Vahab S. Mirrokni and Shang-Hua Teng

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


bibtype: inproceedings
filename: pagerank.bib
label: Andersen:2007:LPD
author: Reid Andersen and Fan Chung and Kevin Lang
title: Local partitioning for directed graphs using PageRank
crossref: Bonato:2007:AMW
pages: 166--178
year: 2007
DOI: https://doi.org/10.1007/978-3-540-77004-6_13
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
MRclass: 05C20 (68M10 68R10 68U35)
MRnumber: MR2504914 (2010f:05082)
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
series: ser-LNCS
acknowledgement: ack-nhfb
rawauthor: Reid Andersen and Fan Chung and Kevin Lang
rawtitle: Local partitioning for directed graphs using {PageRank}
entry:
@InProceedings{Andersen:2007:LPD,
  author =       "Reid Andersen and Fan Chung and Kevin Lang",
  title =        "Local partitioning for directed graphs using
                 {PageRank}",
  crossref =     "Bonato:2007:AMW",
  pages =        "166--178",
  year =         "2007",
  DOI =          "https://doi.org/10.1007/978-3-540-77004-6_13",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "05C20 (68M10 68R10 68U35)",
  MRnumber =     "MR2504914 (2010f:05082)",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: Andersen:2007:UPL
author: Reid Andersen and Fan Chung and Kevin Lang
title: Using PageRank to locally partition a graph
journal: j-INTERNET-MATH
volume: 4
number: 1
pages: 35--64
year: 2007
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 05C70 (05C50 05C85 05C90 68R10)
MRnumber: MR2492174 (2009k:05142)
MRreviewer: Anthony Bonato
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1243430567
fjournal: Internet Mathematics
ZMnumber: 1170.68302
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Reid Andersen and Fan Chung and Kevin Lang
rawtitle: Using {PageRank} to locally partition a graph
entry:
@Article{Andersen:2007:UPL,
  author =       "Reid Andersen and Fan Chung and Kevin Lang",
  title =        "Using {PageRank} to locally partition a graph",
  journal =      j-INTERNET-MATH,
  volume =       "4",
  number =       "1",
  pages =        "35--64",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "05C70 (05C50 05C85 05C90 68R10)",
  MRnumber =     "MR2492174 (2009k:05142)",
  MRreviewer =   "Anthony Bonato",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1243430567",
  ZMnumber =     "1170.68302",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: inproceedings
filename: pagerank.bib
label: Avrachenkov:2007:DPM
author: Konstantin Avrachenkov and Nelly Litvak and Kim Son Pham
title: Distribution of PageRank mass among principle components of the web
crossref: Bonato:2007:AMW
pages: 16--28
year: 2007
DOI: https://doi.org/10.1007/978-3-540-77004-6_2
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
MRclass: 68U35 (15A18)
MRnumber: MR2504904
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
series: ser-LNCS
ZMnumber: 1136.68319
acknowledgement: ack-nhfb
rawauthor: Konstantin Avrachenkov and Nelly Litvak and Kim Son Pham
rawtitle: Distribution of {PageRank} mass among principle components of the web
entry:
@InProceedings{Avrachenkov:2007:DPM,
  author =       "Konstantin Avrachenkov and Nelly Litvak and Kim Son
                 Pham",
  title =        "Distribution of {PageRank} mass among principle
                 components of the web",
  crossref =     "Bonato:2007:AMW",
  pages =        "16--28",
  year =         "2007",
  DOI =          "https://doi.org/10.1007/978-3-540-77004-6_2",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "68U35 (15A18)",
  MRnumber =     "MR2504904",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  ZMnumber =     "1136.68319",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: Avrachenkov:2007:MCM
author: K. Avrachenkov and N. Litvak and D. Nemirovsky and N. Osipova
title: Monte Carlo Methods in PageRank Computation: When One Iteration is Sufficient
journal: j-SIAM-J-NUMER-ANAL
volume: 45
number: 2
pages: 890--904
month: feb
monthnumber: 2
year: 2007
CODEN: SJNAAM
DOI: https://doi.org/10.1137/050643799
ISSN: 0036-1429 (print), 1095-7170 (electronic) OR 00361429 (print), 10957170 (electronic)
ISSNL: 0036-1429 OR 00361429
MRclass: 60J20 (60J10 65C05); 60J20 65C05 60J05 60J10 65C40
MRnumber: MR2300301
bibdate: Sat May 8 18:33:10 MDT 2010
bibtimestamp: 2010.05.08 18:33:10 MDT
URL: https://www.math.utah.edu/pub/tex/bib/siamjnumeranal2000.bib
fjournal: SIAM Journal on Numerical Analysis
keywords: absorbing Markov chains; Google; Monte Carlo methods; PageRank
ZMnumber: 1146.60056
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/sinum
rawauthor: K. Avrachenkov and N. Litvak and D. Nemirovsky and N. Osipova
rawtitle: {Monte Carlo} Methods in {PageRank} Computation: When One Iteration is Sufficient
bibsource:
http://siamdl.aip.org/dbt/dbt.jsp?KEY=SJNAAM&Volume=45&Issue=2; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

abstract:
PageRank is one of the principle criteria according to which Google ranks Web pages. PageRank can be interpreted as a frequency of visiting a Web page by a random surfer, and thus it reflects the popularity of a Web page. Google computes the PageRank using the power iteration method, which requires about one week of intensive computations. In the present work we propose and analyze Monte Carlo-type methods for the PageRank computation. There are several advantages of the probabilistic Monte Carlo methods over the deterministic power iteration method: Monte Carlo methods already provide good estimation of the PageRank for relatively important pages after one iteration; Monte Carlo methods have natural parallel implementation; and finally, Monte Carlo methods allow one to perform continuous update of the PageRank as the structure of the Web changes.

rawabstract:
PageRank is one of the principle criteria according to which Google ranks Web pages. PageRank can be interpreted as a frequency of visiting a Web page by a random surfer, and thus it reflects the popularity of a Web page. Google computes the PageRank using the power iteration method, which requires about one week of intensive computations. In the present work we propose and analyze Monte Carlo-type methods for the PageRank computation. There are several advantages of the probabilistic Monte Carlo methods over the deterministic power iteration method: Monte Carlo methods already provide good estimation of the PageRank for relatively important pages after one iteration; Monte Carlo methods have natural parallel implementation; and finally, Monte Carlo methods allow one to perform continuous update of the PageRank as the structure of the Web changes.

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


bibtype: article
filename: pagerank.bib
label: Bergstrom:2007:EMV
author: C. Bergstrom
title: Eigenfactor: Measuring the value and prestige of scholarly journals
journal: College & Research Libraries News
volume: 68
pages: 5--??
year: 2007
bibdate: Fri Mar 11 16:15:59 2016
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2016.03.11 16:15:59 ???
keywords: PageRank algorithm
acknowledgement: ack-nhfb
rawauthor: C. Bergstrom
rawtitle: Eigenfactor: Measuring the value and prestige of scholarly journals
entry:
@Article{Bergstrom:2007:EMV,
  author =       "C. Bergstrom",
  title =        "Eigenfactor: Measuring the value and prestige of
                 scholarly journals",
  journal =      "College \& Research Libraries News",
  volume =       "68",
  number =       "??",
  pages =        "5--??",
  month =        "????",
  year =         "2007",
  bibdate =      "Fri Mar 11 16:15:59 2016",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank algorithm",
}


bibtype: inproceedings
filename: pagerank.bib
label: Bickson:2007:PPR
author: D. Bickson and D. Malkhi and Lidong Zhou
title: Peer-to-Peer Rating
crossref: Hauswirth:2007:SII
pages: 211--218
year: 2007
DOI: https://doi.org/10.1109/P2P.2007.36
bibdate: Thu May 06 16:58:10 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:58:10 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: D. Bickson and D. Malkhi and Lidong Zhou
rawtitle: Peer-to-Peer Rating
entry:
@InProceedings{Bickson:2007:PPR,
  author =       "D. Bickson and D. Malkhi and Lidong Zhou",
  title =        "Peer-to-Peer Rating",
  crossref =     "Hauswirth:2007:SII",
  pages =        "211--218",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/P2P.2007.36",
  bibdate =      "Thu May 06 16:58:10 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: Bidoki:2007:FFI
author: A. M. Z. Bidoki and N. Yazdani and P. Ghodsnia
title: FICA: a Fast Intelligent Crawling Algorithm
crossref: Lin:2007:PIW
pages: 635--641
year: 2007
DOI: https://doi.org/10.1109/WI.2007.91
bibdate: Thu May 06 16:57:29 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:57:29 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: A. M. Z. Bidoki and N. Yazdani and P. Ghodsnia
rawtitle: {FICA}: a Fast Intelligent Crawling Algorithm
entry:
@InProceedings{Bidoki:2007:FFI,
  author =       "A. M. Z. Bidoki and N. Yazdani and P. Ghodsnia",
  title =        "{FICA}: a Fast Intelligent Crawling Algorithm",
  crossref =     "Lin:2007:PIW",
  pages =        "635--641",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/WI.2007.91",
  bibdate =      "Thu May 06 16:57:29 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: inbook
filename: pagerank.bib
label: Boldi:2007:DIP
author: Paolo Boldi and Massimo Santini and Sebastiano Vigna
title: A deeper investigation of PageRank as a function of the damping factor
volume: 07071
publisher: International Begegnungs- und Forschungszentrum fur Informatik
address: Wadern, Germany
year: 2007
bibdate: Fri Feb 19 15:32:30 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 15:32:30 ???
series: Dagstuhl seminar proceedings
URL: http://drops.dagstuhl.de/opus/volltexte/2007/1072/pdf/07071.VignaSebastiano.Paper.1072
acknowledgement: ack-nhfb
rawauthor: Paolo Boldi and Massimo Santini and Sebastiano Vigna
rawtitle: A deeper investigation of {PageRank} as a function of the damping factor
entry:
@InBook{Boldi:2007:DIP,
  author =       "Paolo Boldi and Massimo Santini and Sebastiano Vigna",
  title =        "A deeper investigation of {PageRank} as a function of
                 the damping factor",
  volume =       "07071",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "????",
  year =         "2007",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Feb 19 15:32:30 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2007/1072/pdf/07071.VignaSebastiano.Paper.1072",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Boldi:2007:TPT
author: Paolo Boldi and Roberto Posenato and Massimo Santini and Sebastiano Vigna
title: Traps and pitfalls of topic-biased PageRank
volume: 4936
publisher: pub-SV
address: pub-SV:adr
pages: 107--116
year: 2007
DOI: https://doi.org/10.1007/978-3-540-78808-9_10
ISBN: 3-540-78808-5 OR 3540788085
ISBN13: 978-3-540-78808-9 OR 9783540788089
bibdate: Tue Aug 11 18:00:34 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 18:00:34 ???
series: ser-LNCS
URL: http://link.springer.com/chapter/10.1007/978-3-540-78808-9_10
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1007/978-3-540-78808-9
bookpages: x + 165
rawauthor: Paolo Boldi and Roberto Posenato and Massimo Santini and Sebastiano Vigna
rawtitle: Traps and pitfalls of topic-biased {PageRank}
editor:
David Hutchison and William Aiello and Andrei Broder and Jeannette Janssen and Takeo Kanade and Josef Kittler and Jon M. Kleinberg and Friedemann Mattern and Evangelos Milios and John C. Mitchell and Moni Naor and Oscar Nierstrasz and C. Pandu Rangan and Bernhard Steffen and Madhu Sudan and Demetri Terzopoulos and Doug Tygar and Moshe Y. Vardi and Gerhard Weikum

booktitle:
Algorithms and Models for the Web-Graph $h [Elektronische Ressource]: Fourth International Workshop, WAW 2006, Banff, Canada, November 30--December 1, 2006. Revised Papers

TOC:
Modelling and Mining of Networked Information Spaces Workshop on Algorithms and Models for the Web Graph Expansion and Lack Thereof in Randomly Perturbed Graphs Web Structure in 2005 Local/Global Phenomena in Geometrically Generated Graphs Approximating PageRank from In-Degree Probabilistic Relation between In-Degree and PageRank Communities in Large Networks: Identification and Ranking Combating Spamdexing: Incorporating Heuristics in Link-Based Ranking Traps and Pitfalls of Topic-Biased PageRank A Scalable Multilevel Algorithm for Graph Clustering and Community Structure Detection A Phrase Recommendation Algorithm Based on Query Stream Mining in Web Search Engines Characterization of Graphs Using Degree Cores Web Structure Mining by Isolated Stars Representing and Quantifying Rank Change for the Web Graph

rawbooktitle:
{Algorithms and Models for the Web-Graph \$h [Elektronische Ressource]: Fourth International Workshop, WAW 2006, Banff, Canada, November 30--December 1, 2006. Revised Papers}

raweditor:
David Hutchison and William Aiello and Andrei Broder and Jeannette Janssen and Takeo Kanade and Josef Kittler and Jon M. Kleinberg and Friedemann Mattern and Evangelos Milios and John C. Mitchell and Moni Naor and Oscar Nierstrasz and C. {Pandu Rangan} and Bernhard Steffen and Madhu Sudan and Demetri Terzopoulos and Doug Tygar and Moshe Y. Vardi and Gerhard Weikum

rawTOC:
Modelling and Mining of Networked Information Spaces \\ Workshop on Algorithms and Models for the Web Graph \\ Expansion and Lack Thereof in Randomly Perturbed Graphs \\ Web Structure in 2005 \\ Local/Global Phenomena in Geometrically Generated Graphs \\ Approximating PageRank from In-Degree \\ Probabilistic Relation between In-Degree and PageRank \\ Communities in Large Networks: Identification and Ranking \\ Combating Spamdexing: Incorporating Heuristics in Link-Based Ranking \\ Traps and Pitfalls of Topic-Biased PageRank \\ A Scalable Multilevel Algorithm for Graph Clustering and Community Structure Detection \\ A Phrase Recommendation Algorithm Based on Query Stream Mining in Web Search Engines \\ Characterization of Graphs Using Degree Cores \\ Web Structure Mining by Isolated Stars \\ Representing and Quantifying Rank \\ Change for the Web Graph

entry:
@InProceedings{Boldi:2007:TPT,
  author =       "Paolo Boldi and Roberto Posenato and Massimo Santini
                 and Sebastiano Vigna",
  editor =       "David Hutchison and William Aiello and Andrei Broder
                 and Jeannette Janssen and Takeo Kanade and Josef
                 Kittler and Jon M. Kleinberg and Friedemann Mattern and
                 Evangelos Milios and John C. Mitchell and Moni Naor and
                 Oscar Nierstrasz and C. {Pandu Rangan} and Bernhard
                 Steffen and Madhu Sudan and Demetri Terzopoulos and
                 Doug Tygar and Moshe Y. Vardi and Gerhard Weikum",
  booktitle =    "{Algorithms and Models for the Web-Graph \$h
                 [Elektronische Ressource]: Fourth International
                 Workshop, WAW 2006, Banff, Canada, November
                 30--December 1, 2006. Revised Papers}",
  title =        "Traps and pitfalls of topic-biased {PageRank}",
  volume =       "4936",
  publisher =    pub-SV,
  address =      pub-SV:adr,
  pages =        "107--116",
  year =         "2007",
  DOI =          "https://doi.org/10.1007/978-3-540-78808-9_10",
  ISBN =         "3-540-78808-5",
  ISBN-13 =      "978-3-540-78808-9",
  bibdate =      "Tue Aug 11 18:00:34 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  URL =          "http://link.springer.com/chapter/10.1007/978-3-540-78808-9_10",
  acknowledgement = ack-nhfb,
  book-DOI =     "https://doi.org/10.1007/978-3-540-78808-9",
  bookpages =    "x + 165",
  tableofcontents = "Modelling and Mining of Networked Information
                 Spaces \\
                 Workshop on Algorithms and Models for the Web Graph \\
                 Expansion and Lack Thereof in Randomly Perturbed Graphs
                 \\
                 Web Structure in 2005 \\
                 Local/Global Phenomena in Geometrically Generated
                 Graphs \\
                 Approximating PageRank from In-Degree \\
                 Probabilistic Relation between In-Degree and PageRank
                 \\
                 Communities in Large Networks: Identification and
                 Ranking \\
                 Combating Spamdexing: Incorporating Heuristics in
                 Link-Based Ranking \\
                 Traps and Pitfalls of Topic-Biased PageRank \\
                 A Scalable Multilevel Algorithm for Graph Clustering
                 and Community Structure Detection \\
                 A Phrase Recommendation Algorithm Based on Query Stream
                 Mining in Web Search Engines \\
                 Characterization of Graphs Using Degree Cores \\
                 Web Structure Mining by Isolated Stars \\
                 Representing and Quantifying Rank \\
                 Change for the Web Graph",
}


bibtype: incollection
filename: pagerank.bib
label: Brezinski:2007:EMP
author: Claude Brezinski and Michela Redivo-Zaglia
editor: Andreas Frommer and Michael W. Mahoney and Daniel B. Szyld
booktitle: Web Information Retrieval and Linear Algebra Algorithms
title: Extrapolation and minimization procedures for the PageRank vector
volume: 07071
publisher: International Begegnungs- und Forschungszentrum fur Informatik
address: Wadern, Germany
pages: 1862--????
year: 2007
bibdate: Fri Feb 19 15:32:30 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 15:32:30 ???
series: Dagstuhl seminar proceedings
URL: http://drops.dagstuhl.de/opus/volltexte/2007/1068/pdf/07071.RedivoZagliaMichela.Paper.1068
acknowledgement: ack-nhfb
rawauthor: Claude Brezinski and Michela Redivo-Zaglia
rawbooktitle: {Web} Information Retrieval and Linear Algebra Algorithms
raweditor: Andreas Frommer and Michael W. Mahoney and Daniel B. Szyld
rawtitle: Extrapolation and minimization procedures for the {PageRank} vector
entry:
@InCollection{Brezinski:2007:EMP,
  author =       "Claude Brezinski and Michela Redivo-Zaglia",
  editor =       "Andreas Frommer and Michael W. Mahoney and Daniel B.
                 Szyld",
  booktitle =    "{Web} Information Retrieval and Linear Algebra
                 Algorithms",
  title =        "Extrapolation and minimization procedures for the
                 {PageRank} vector",
  volume =       "07071",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "1862--????",
  year =         "2007",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Feb 19 15:32:30 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2007/1068/pdf/07071.RedivoZagliaMichela.Paper.1068",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Caverlee:2007:SRW
author: J. Caverlee and S. Webb and L. Liu
title: Spam-Resilient Web Rankings via Influence Throttling
crossref: IEEE:2007:ICI
pages: 1--10
year: 2007
DOI: https://doi.org/10.1109/IPDPS.2007.370233
bibdate: Thu May 06 15:12:50 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:12:50 ???
acknowledgement: ack-nhfb
rawauthor: J. Caverlee and S. Webb and L. Liu
rawtitle: Spam-Resilient {Web} Rankings via Influence Throttling
entry:
@InProceedings{Caverlee:2007:SRW,
  author =       "J. Caverlee and S. Webb and L. Liu",
  title =        "Spam-Resilient {Web} Rankings via Influence
                 Throttling",
  crossref =     "IEEE:2007:ICI",
  pages =        "1--10",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/IPDPS.2007.370233",
  bibdate =      "Thu May 06 15:12:50 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Chakrabarti:2007:DPP
author: Soumen Chakrabarti
editor: ACM
booktitle: Proceedings of the 16th international conference on World Wide Web
title: Dynamic personalized PageRank in entity-relation graphs
publisher: pub-ACM
address: pub-ACM:adr
pages: 571--580
year: 2007
DOI: https://doi.org/10.1016/S0306-4573(96)85003-5
ISBN: 1-59593-654-8 OR 1595936548
ISBN13: 978-1-59593-654-7 OR 9781595936547
bibdate: Sat May 8 18:33:07 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:07 MDT
keywords: graph proximity search; personalized pagerank
acknowledgement: ack-nhfb
rawauthor: Soumen Chakrabarti
rawbooktitle: Proceedings of the 16th international conference on World Wide Web
raweditor: {ACM}
rawtitle: Dynamic personalized {PageRank} in entity-relation graphs
abstract:
Extractors and taggers turn unstructured text into entity-relation(ER) graphs where nodes are entities (email, paper, person,conference, company) and edges are relations (wrote, cited,works-for). Typed proximity search of the form bf type=person NEAR company~'IBM', paper~'XML' is an increasingly useful search paradigm in ER graphs. Proximity search implementations either perform a Pagerank-like computation at query time, which is slow, or precompute, store and combine per-word Pageranks, which can be very expensive in terms of preprocessing time and space. We present HubRank, a new system for fast, dynamic, space-efficient proximity searches in ER graphs. During preprocessing, HubRank computes and indexes certain 'sketchy' random walk fingerprints for a small fraction of nodes, carefully chosen using query log statistics. At query time, a small 'active' subgraph is identified, bordered by nodes with indexed fingerprints. These fingerprints are adaptively loaded to various resolutions to form approximate personalized Pagerank vectors (PPVs). PPVs at remaining active nodes are now computed iteratively. We report on experiments with CiteSeer's ER graph and millions of real Cite Seer queries. Some representative numbers follow. On our testbed, HubRank preprocesses and indexes 52 times faster than whole-vocabulary PPV computation. A text index occupies 56 MB. Whole-vocabulary PPVs would consume 102GB. If PPVs are truncated to 56 MB, precision compared to true Pagerank drops to 0.55; in contrast, HubRank has precision 0.91 at 63MB. HubRank's average query time is 200-300 milliseconds; query-time Pagerank computation takes 11 seconds on average.

rawabstract:
Extractors and taggers turn unstructured text into entity-relation(ER) graphs where nodes are entities (email, paper, person,conference, company) and edges are relations (wrote, cited,works-for). Typed proximity search of the form {\bf type=person NEAR company~'IBM', paper~'XML'} is an increasingly useful search paradigm in ER graphs. Proximity search implementations either perform a Pagerank-like computation at query time, which is slow, or precompute, store and combine per-word Pageranks, which can be very expensive in terms of preprocessing time and space. We present HubRank, a new system for fast, dynamic, space-efficient proximity searches in ER graphs. During preprocessing, HubRank computes and indexes certain 'sketchy' random walk fingerprints for a small fraction of nodes, carefully chosen using query log statistics. At query time, a small 'active' subgraph is identified, bordered by nodes with indexed fingerprints. These fingerprints are adaptively loaded to various resolutions to form approximate personalized Pagerank vectors (PPVs). PPVs at remaining active nodes are now computed iteratively. We report on experiments with CiteSeer's ER graph and millions of real Cite Seer queries. Some representative numbers follow. On our testbed, HubRank preprocesses and indexes 52 times faster than whole-vocabulary PPV computation. A text index occupies 56 MB. Whole-vocabulary PPVs would consume 102GB. If PPVs are truncated to 56 MB, precision compared to true Pagerank drops to 0.55; in contrast, HubRank has precision 0.91 at 63MB. HubRank's average query time is 200-300 milliseconds; query-time Pagerank computation takes 11 seconds on average.

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


bibtype: article
filename: pagerank.bib
label: Chau:2007:IWA
author: M. Chau and H. Chen
title: Incorporating Web Analysis Into Neural Networks: An Example in Hopfield Net Searching
journal: IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
volume: 37
number: 3
pages: 352--358
month: may
monthnumber: 5
year: 2007
DOI: https://doi.org/10.1109/TSMCC.2007.893277
ISSN: 1094-6977 OR 10946977
bibdate: Thu May 06 16:34:52 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:34:52 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: M. Chau and H. Chen
rawtitle: Incorporating {Web} Analysis Into Neural Networks: An Example in {Hopfield} Net Searching
entry:
@Article{Chau:2007:IWA,
  author =       "M. Chau and H. Chen",
  title =        "Incorporating {Web} Analysis Into Neural Networks: An
                 Example in {Hopfield} Net Searching",
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics,
                 Part C: Applications and Reviews",
  volume =       "37",
  number =       "3",
  pages =        "352--358",
  month =        may,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1109/TSMCC.2007.893277",
  ISSN =         "1094-6977",
  bibdate =      "Thu May 06 16:34:52 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: article
filename: pagerank.bib
label: Chen:2007:FSG
author: P. Chen and H. Xie and S. Maslov and S. Redner
title: Finding scientific gems with Google's PageRank algorithm
journal: j-J-INFORMETRICS
volume: 1
number: 1
pages: 8--15
month: jan
monthnumber: 1
year: 2007
DOI: https://doi.org/10.1016/j.joi.2006.06.001
ISSN: 1751-1577 (print), 1875-5879 (electronic) OR 17511577 (print), 18755879 (electronic)
ISSNL: 1751-1577 OR 17511577
bibdate: Tue Aug 11 16:19:16 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:19:16 ???
URL: http://www.sciencedirect.com/science/article/pii/S1751157706000034
fjournal: Journal of Informetrics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/17511577
rawauthor: P. Chen and H. Xie and S. Maslov and S. Redner
rawtitle: Finding scientific gems with {Google}'s {PageRank} algorithm
entry:
@Article{Chen:2007:FSG,
  author =       "P. Chen and H. Xie and S. Maslov and S. Redner",
  title =        "Finding scientific gems with {Google}'s {PageRank}
                 algorithm",
  journal =      j-J-INFORMETRICS,
  volume =       "1",
  number =       "1",
  pages =        "8--15",
  month =        jan,
  year =         "2007",
  DOI =          "https://doi.org/10.1016/j.joi.2006.06.001",
  ISSN =         "1751-1577 (print), 1875-5879 (electronic)",
  ISSN-L =       "1751-1577",
  bibdate =      "Tue Aug 11 16:19:16 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1751157706000034",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Informetrics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/17511577",
}


bibtype: article
filename: pagerank.bib
label: Chung:2007:HKP
author: Fan Chung
title: The heat kernel as the pagerank of a graph
journal: j-PROC-NATL-ACAD-SCI-USA
volume: 104
number: 50
pages: 19735--19740
day: 11
month: dec
monthnumber: 12
year: 2007
CODEN: PNASA6
DOI: https://doi.org/10.1073/pnas.0708838104
ISSN: 0027-8424 (print), 1091-6490 (electronic) OR 00278424 (print), 10916490 (electronic)
ISSNL: 0027-8424 OR 00278424
bibdate: Fri Jun 3 10:03:23 MDT 2011
bibsource: fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 10:03:23 MDT
URL: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2148367
fjournal: Proceedings of the National Academy of Sciences of the United States of America
acknowledgement: ack-nhfb
journalURL: http://www.pnas.org/search
rawauthor: Fan Chung
rawtitle: The heat kernel as the pagerank of a graph
abstract:
The concept of pagerank was first started as a way for determining the ranking of Web pages by Web search engines. Based on relations in interconnected networks, pagerank has become a major tool for addressing fundamental problems arising in general graphs, especially for large information networks with hundreds of thousands of nodes. A notable notion of pagerank, introduced by Brin and Page and denoted by PageRank, is based on random walks as a geometric sum. In this paper, we consider a notion of pagerank that is based on the (discrete) heat kernel and can be expressed as an exponential sum of random walks. The heat kernel satisfies the heat equation and can be used to analyze many useful properties of random walks in a graph. A local Cheeger inequality is established, which implies that, by focusing on cuts determined by linear orderings of vertices using the heat kernel pageranks, the resulting partition is within a quadratic factor of the optimum. This is true, even if we restrict the volume of the small part separated by the cut to be close to some specified target value. This leads to a graph partitioning algorithm for which the running time is proportional to the size of the targeted volume (instead of the size of the whole graph).

rawabstract:
The concept of pagerank was first started as a way for determining the ranking of Web pages by Web search engines. Based on relations in interconnected networks, pagerank has become a major tool for addressing fundamental problems arising in general graphs, especially for large information networks with hundreds of thousands of nodes. A notable notion of pagerank, introduced by Brin and Page and denoted by PageRank, is based on random walks as a geometric sum. In this paper, we consider a notion of pagerank that is based on the (discrete) heat kernel and can be expressed as an exponential sum of random walks. The heat kernel satisfies the heat equation and can be used to analyze many useful properties of random walks in a graph. A local Cheeger inequality is established, which implies that, by focusing on cuts determined by linear orderings of vertices using the heat kernel pageranks, the resulting partition is within a quadratic factor of the optimum. This is true, even if we restrict the volume of the small part separated by the cut to be close to some specified target value. This leads to a graph partitioning algorithm for which the running time is proportional to the size of the targeted volume (instead of the size of the whole graph).

entry:
@Article{Chung:2007:HKP,
  author =       "Fan Chung",
  title =        "The heat kernel as the pagerank of a graph",
  journal =      j-PROC-NATL-ACAD-SCI-USA,
  volume =       "104",
  number =       "50",
  pages =        "19735--19740",
  day =          "11",
  month =        dec,
  year =         "2007",
  CODEN =        "PNASA6",
  DOI =          "https://doi.org/10.1073/pnas.0708838104",
  ISSN =         "0027-8424 (print), 1091-6490 (electronic)",
  ISSN-L =       "0027-8424",
  bibdate =      "Fri Jun 3 10:03:23 MDT 2011",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2148367",
  abstract =     "The concept of pagerank was first started as a way for
                 determining the ranking of Web pages by Web search
                 engines. Based on relations in interconnected networks,
                 pagerank has become a major tool for addressing
                 fundamental problems arising in general graphs,
                 especially for large information networks with hundreds
                 of thousands of nodes. A notable notion of pagerank,
                 introduced by Brin and Page and denoted by PageRank, is
                 based on random walks as a geometric sum. In this
                 paper, we consider a notion of pagerank that is based
                 on the (discrete) heat kernel and can be expressed as
                 an exponential sum of random walks. The heat kernel
                 satisfies the heat equation and can be used to analyze
                 many useful properties of random walks in a graph. A
                 local Cheeger inequality is established, which implies
                 that, by focusing on cuts determined by linear
                 orderings of vertices using the heat kernel pageranks,
                 the resulting partition is within a quadratic factor of
                 the optimum. This is true, even if we restrict the
                 volume of the small part separated by the cut to be
                 close to some specified target value. This leads to a
                 graph partitioning algorithm for which the running time
                 is proportional to the size of the targeted volume
                 (instead of the size of the whole graph).",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the National Academy of Sciences of the
                 United States of America",
  journal-URL =  "http://www.pnas.org/search",
}


bibtype: inproceedings
filename: pagerank.bib
label: Constantine:2007:UPC
author: Paul G. Constantine and David F. Gleich
title: Using polynomial chaos to compute the influence of multiple random surfers in the PageRank model
crossref: Bonato:2007:AMW
pages: 82--95
year: 2007
DOI: https://doi.org/10.1007/978-3-540-77004-6_7
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
MRclass: 68U35 (60G99 65C05 68W40)
MRnumber: MR2505172
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
series: ser-LNCS
ZMnumber: 1136.68321
acknowledgement: ack-nhfb
rawauthor: Paul G. Constantine and David F. Gleich
rawtitle: Using polynomial chaos to compute the influence of multiple random surfers in the {PageRank} model
entry:
@InProceedings{Constantine:2007:UPC,
  author =       "Paul G. Constantine and David F. Gleich",
  title =        "Using polynomial chaos to compute the influence of
                 multiple random surfers in the {PageRank} model",
  crossref =     "Bonato:2007:AMW",
  pages =        "82--95",
  year =         "2007",
  DOI =          "https://doi.org/10.1007/978-3-540-77004-6_7",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "68U35 (60G99 65C05 68W40)",
  MRnumber =     "MR2505172",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  ZMnumber =     "1136.68321",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Costache:2007:PPB
author: Stefania Costache and Wolfgang Nejdl and Raluca Paiu
editor: Anonymous
booktitle: Proceedings of the 19th International Conference on Advanced Information Systems Engineering
title: Personalizing PageRank-based ranking over distributed collections
publisher: pub-SV
address: pub-SV:adr
pages: 111--126
year: 2007
DOI: https://doi.org/10.1145/511446.511513
ISBN: 0-7918-4804-3 OR 0791848043
ISBN13: 978-0-7918-4804-3 OR 9780791848043
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
LCCN: TA174 .D4623 2007
bibdate: Sat May 8 18:33:11 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:11 MDT
series: ser-LNCS
keywords: distributed search; pagerank; personalization; privacy
acknowledgement: ack-nhfb
rawauthor: Stefania Costache and Wolfgang Nejdl and Raluca Paiu
rawbooktitle: Proceedings of the 19th International Conference on Advanced Information Systems Engineering
raweditor: Anonymous
rawtitle: Personalizing {PageRank-based} ranking over distributed collections
abstract:
In distributed work environments, where users are sharing and searching resources, ensuring an appropriate ranking at remote peers is a key problem. While this issue has been investigated for federated libraries, where the exchange of collection specific information suffices to enable homogeneous TFxIDF rankings across the participating collections, no solutions are known for PageRank-based ranking schemes, important for personalized retrieval on the desktop.par Connected users share fulltext resources and metadata expressing information about them and connecting them. Based on which information is shared or private, we propose several algorithms for computing personalized PageRank-based rankings for these connected peers. We discuss which information is needed for the ranking computation and how Page-Rank values can be estimated in case of incomplete information. We analyze the performance of our algorithms through a set of experiments, and conclude with suggestions for choosing among these algorithms.

rawabstract:
In distributed work environments, where users are sharing and searching resources, ensuring an appropriate ranking at remote peers is a key problem. While this issue has been investigated for federated libraries, where the exchange of collection specific information suffices to enable homogeneous TFxIDF rankings across the participating collections, no solutions are known for PageRank-based ranking schemes, important for personalized retrieval on the desktop.\par Connected users share fulltext resources and metadata expressing information about them and connecting them. Based on which information is shared or private, we propose several algorithms for computing personalized PageRank-based rankings for these connected peers. We discuss which information is needed for the ranking computation and how Page-Rank values can be estimated in case of incomplete information. We analyze the performance of our algorithms through a set of experiments, and conclude with suggestions for choosing among these algorithms.

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

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


bibtype: article
filename: pagerank.bib
label: DelCorso:2007:CKS
author: Gianna M. Del Corso and Antonio Gulli and Francesco Romani
title: Comparison of Krylov subspace methods on the PageRank problem
journal: j-J-COMPUT-APPL-MATH
volume: 210
number: 1--2
pages: 159--166
year: 2007
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2006.10.080
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
MRclass: 65F15 (65Y20)
MRnumber: MR2389165 (2009b:65096)
MRreviewer: Valeria Ruggiero
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
fjournal: Journal of Computational and Applied Mathematics
ZMnumber: 1134.65026
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Gianna M. {Del Corso} and Antonio Gull{\'\i} and Francesco Romani
rawtitle: Comparison of {Krylov} subspace methods on the {PageRank} problem
abstract:
PageRank algorithm plays a very important role in search engine technology and consists in the computation of the eigenvector corresponding to the eigenvalue one of a matrix whose size is now in the billions. The problem incorporates a parameter @a that determines the difficulty of the problem. In this paper, the effectiveness of stationary and nonstationary methods are compared on some portion of real web matrices for different choices of @a. We see that stationary methods are very reliable and more competitive when the problem is well conditioned, that is for small values of @a. However, for large values of the parameter @a the problem becomes more difficult and methods such as preconditioned BiCGStab or restarted preconditioned GMRES become competitive with stationary methods in terms of Mflops count as well as in number of iterations necessary to reach convergence.

rawabstract:
PageRank algorithm plays a very important role in search engine technology and consists in the computation of the eigenvector corresponding to the eigenvalue one of a matrix whose size is now in the billions. The problem incorporates a parameter @a that determines the difficulty of the problem. In this paper, the effectiveness of stationary and nonstationary methods are compared on some portion of real web matrices for different choices of @a. We see that stationary methods are very reliable and more competitive when the problem is well conditioned, that is for small values of @a. However, for large values of the parameter @a the problem becomes more difficult and methods such as preconditioned BiCGStab or restarted preconditioned GMRES become competitive with stationary methods in terms of Mflops count as well as in number of iterations necessary to reach convergence.

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


bibtype: article
filename: pagerank.bib
label: Djerassi:2007:BRW
author: Carl Djerassi
journal: j-PHYS-TODAY
volume: 60
number: 2
pages: 63--64
year: 2007
CODEN: PHTOAD
DOI: https://doi.org/10.1063/1.2711638
ISSN: 0031-9228 (print), 1945-0699 (electronic) OR 00319228 (print), 19450699 (electronic)
ISSNL: 0031-9228 OR 00319228
bibdate: Wed Sep 12 15:15:45 MDT 2012
bibtimestamp: 2012.09.12 15:15:45 MDT
URL: http://link.aip.org/link/phtoad/v60/i2/p63/s1
fjournal: Physics Today
keywords: Copenhagen; Michael Frayn; Niels Bohr; Werner Heisenberg
acknowledgement: ack-nhfb
journalURL: http://www.physicstoday.org/
rawauthor: Carl Djerassi
title:
Book Reviews: When acting speaks louder than words: Science on Stage: booktitle From `Doctor Faustus' to `Copenhagen', by Kirsten Shepherd-Barr. booktitle Google's PageRank and Beyond: The Science of Search Engine Rankings, by Amy N. Langville and Carl D. Meyer. booktitle Broken Genius The Rise and Fall of William Shockley, Creator of the Electronic Age, by Joel N. Shurkin

bibsource:
https://www.math.utah.edu/pub/bibnet/authors/b/bohr-niels.bib; https://www.math.utah.edu/pub/bibnet/authors/h/heisenberg-werner.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

rawtitle:
Book Reviews: When acting speaks louder than words: Science on Stage: {{\booktitle{From `Doctor Faustus' to `Copenhagen'}}, by Kirsten Shepherd-Barr. \booktitle{Google's PageRank and Beyond: The Science of Search Engine Rankings}, by Amy N. Langville and Carl D. Meyer. \booktitle{Broken Genius The Rise and Fall of William Shockley, Creator of the Electronic Age}, by Joel N. Shurkin}

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


bibtype: article
filename: pagerank.bib
label: Donato:2007:WGH
author: Debora Donato and Luigi Laura and Stefano Leonardi and Stefano Millozzi
title: The Web as a graph: How far we are
journal: j-TOIT
volume: 7
number: 1
pages: 4:1--4:??
month: feb
monthnumber: 2
year: 2007
DOI: https://doi.org/10.1145/1189740.1189744
ISSN: 1533-5399 (print), 1557-6051 (electronic) OR 15335399 (print), 15576051 (electronic)
ISSNL: 1533-5399 OR 15335399
bibdate: Mon Jun 16 10:57:52 MDT 2008
bibsource: http://www.acm.org/pubs/contents/journals/toit/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2008.06.16 10:57:52 MDT
URL: https://www.math.utah.edu/pub/tex/bib/toit.bib
fjournal: ACM Transactions on Internet Technology (TOIT)
keywords: graph structure; models; World-Wide-Web
acknowledgement: ack-nhfb
articleno: 4
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J780
rawauthor: Debora Donato and Luigi Laura and Stefano Leonardi and Stefano Millozzi
rawtitle: The {Web} as a graph: {How} far we are
abstract:
In this article we present an experimental study of the properties of webgraphs. We study a large crawl from 2001 of 200M pages and about 1.4 billion edges, made available by the WebBase project at Stanford, as well as several synthetic ones generated according to various models proposed recently. We investigate several topological properties of such graphs, including the number of bipartite cores and strongly connected components, the distribution of degrees and PageRank values and some correlations; we present a comparison study of the models against these measures.Our findings are that (i) the WebBase sample differs slightly from the (older) samples studied in the literature, and (ii) despite the fact that these models do not catch all of its properties, they do exhibit some peculiar behaviors not found, for example, in the models from classical random graph theory.Moreover we developed a software library able to generate and measure massive graphs in secondary memory; this library is publicy available under the GPL licence. We discuss its implementation and some computational issues related to secondary memory graph algorithms.

rawabstract:
In this article we present an experimental study of the properties of webgraphs. We study a large crawl from 2001 of 200M pages and about 1.4 billion edges, made available by the WebBase project at Stanford, as well as several synthetic ones generated according to various models proposed recently. We investigate several topological properties of such graphs, including the number of bipartite cores and strongly connected components, the distribution of degrees and PageRank values and some correlations; we present a comparison study of the models against these measures.Our findings are that (i) the WebBase sample differs slightly from the (older) samples studied in the literature, and (ii) despite the fact that these models do not catch all of its properties, they do exhibit some peculiar behaviors not found, for example, in the models from classical random graph theory.Moreover we developed a software library able to generate and measure massive graphs in secondary memory; this library is publicy available under the GPL licence. We discuss its implementation and some computational issues related to secondary memory graph algorithms.

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


bibtype: article
filename: pagerank.bib
label: Douglis:2007:ECW
author: Fred Douglis
title: From the Editor in Chief: What's Your PageRank?
journal: j-IEEE-INTERNET-COMPUT
volume: 11
number: 4
pages: 3--4
month: jul
monthnumber: 7
year: 2007
CODEN: IICOFX
DOI: https://doi.org/10.1109/MIC.2007.82
ISSN: 1089-7801 OR 10897801
ISSNL: 1089-7801 OR 10897801
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=4270541
fjournal: IEEE Internet Computing
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4236
rawauthor: Fred Douglis
rawtitle: From the {Editor in Chief}: What's Your {PageRank}?
entry:
@Article{Douglis:2007:ECW,
  author =       "Fred Douglis",
  title =        "From the {Editor in Chief}: What's Your {PageRank}?",
  journal =      j-IEEE-INTERNET-COMPUT,
  volume =       "11",
  number =       "4",
  pages =        "3--4",
  month =        jul # "\slash " # aug,
  year =         "2007",
  CODEN =        "IICOFX",
  DOI =          "https://doi.org/10.1109/MIC.2007.82",
  ISSN =         "1089-7801",
  ISSN-L =       "1089-7801",
  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=4270541",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4236",
  fjournal =     "IEEE Internet Computing",
}


bibtype: inproceedings
filename: pagerank.bib
label: Du:2007:USF
author: Ye Du and Yaoyun Shi and Xin Zhao
editor: ACM
title: Using spam farm to boost PageRank
publisher: pub-ACM
address: pub-ACM:adr
pages: 29--36
year: 2007
DOI: https://doi.org/10.1145/1062745.1062762
ISBN: 1-59593-732-3 OR 1595937323
ISBN13: 978-1-59593-732-2 OR 9781595937322
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
keywords: link spamming; Markov chain; PageRank algorithm
acknowledgement: ack-nhfb
rawauthor: Ye Du and Yaoyun Shi and Xin Zhao
raweditor: {ACM}
rawtitle: Using spam farm to boost {PageRank}
booktitle:
AIRWeb; Vol. 215 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web

abstract:
Nowadays web spamming has emerged to take the economic advantage of high search rankings and threatened the accuracy and fairness of those rankings. Understanding spamming techniques is essential for evaluating the strength and weakness of a ranking algorithm, and for fighting against web spamming. In this paper, we identify the optimal spam farm structure under some realistic assumptions in the single target spam farm model. Our result extends the optimal spam farm claimed by Gyongyi and Garcia-Molina through dropping the assumption that leakage is constant. We also characterize the optimal spam farms under additional constraints, which the spammer may deploy to disguise the spam farm by deviating from the unconstrained optimal structure.

rawabstract:
Nowadays web spamming has emerged to take the economic advantage of high search rankings and threatened the accuracy and fairness of those rankings. Understanding spamming techniques is essential for evaluating the strength and weakness of a ranking algorithm, and for fighting against web spamming. In this paper, we identify the optimal spam farm structure under some realistic assumptions in the single target spam farm model. Our result extends the optimal spam farm claimed by Gy{\"o}ngyi and Garcia-Molina through dropping the assumption that leakage is constant. We also characterize the optimal spam farms under additional constraints, which the spammer may deploy to disguise the spam farm by deviating from the unconstrained optimal structure.

rawbooktitle:
AIRWeb; Vol. 215 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web

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


bibtype: article
filename: pagerank.bib
label: Eirinaki:2007:WSP
author: Magdalini Eirinaki and Michalis Vazirgiannis
title: Web site personalization based on link analysis and navigational patterns
journal: j-TOIT
volume: 7
number: 4
pages: 21:1--21:??
month: oct
monthnumber: 10
year: 2007
DOI: https://doi.org/10.1145/1278366.1278370
ISSN: 1533-5399 (print), 1557-6051 (electronic) OR 15335399 (print), 15576051 (electronic)
ISSNL: 1533-5399 OR 15335399
bibdate: Mon Jun 16 10:58:47 MDT 2008
bibsource: http://www.acm.org/pubs/contents/journals/toit/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2008.06.16 10:58:47 MDT
URL: https://www.math.utah.edu/pub/tex/bib/toit.bib
fjournal: ACM Transactions on Internet Technology (TOIT)
keywords: link analysis; Markov models; recommendations; usage-based PageRank; Web personalization
acknowledgement: ack-nhfb
articleno: 21
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J780
rawauthor: Magdalini Eirinaki and Michalis Vazirgiannis
rawtitle: {Web} site personalization based on link analysis and navigational patterns
abstract:
The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of online information services. The need for predicting the users' needs in order to improve the usability and user retention of a Web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing ``next'' pages to users based on their current visit and past users' navigational patterns. In the vast majority of related algorithms, however, only the usage data is used to produce recommendations, disregarding the structural properties of the Web graph. Thus important---in terms of PageRank authority score---pages may be underrated. In this work, we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to Web pages based on their importance in the Web site's navigational graph. We propose the application of a localized version of UPR ( l-UPR ) to personalized navigational subgraphs for online Web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches.

rawabstract:
The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of online information services. The need for predicting the users' needs in order to improve the usability and user retention of a Web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing ``next'' pages to users based on their current visit and past users' navigational patterns. In the vast majority of related algorithms, however, only the usage data is used to produce recommendations, disregarding the structural properties of the Web graph. Thus important---in terms of PageRank authority score---pages may be underrated. In this work, we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to Web pages based on their importance in the Web site's navigational graph. We propose the application of a localized version of UPR ( l-UPR ) to personalized navigational subgraphs for online Web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches.

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


bibtype: article
filename: pagerank.bib
label: Fortunato:2007:LEP
author: Santo Fortunato and Marian Boguna and Alessandro Flammini and Filippo Menczer
title: On local estimations of PageRank: a mean field approach
journal: j-INTERNET-MATH
volume: 4
number: 2--3
pages: 245--266
year: 2007
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 60G50 (60J20 68M10)
MRnumber: MR2522878
bibdate: Wed May 5 19:28:04 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:04 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1243430608
fjournal: Internet Mathematics
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Santo Fortunato and Mari{\'a}n Bogu{\~n}{\'a} and Alessandro Flammini and Filippo Menczer
rawtitle: On local estimations of {PageRank}: a mean field approach
entry:
@Article{Fortunato:2007:LEP,
  author =       "Santo Fortunato and Mari{\'a}n Bogu{\~n}{\'a} and
                 Alessandro Flammini and Filippo Menczer",
  title =        "On local estimations of {PageRank}: a mean field
                 approach",
  journal =      j-INTERNET-MATH,
  volume =       "4",
  number =       "2--3",
  pages =        "245--266",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "60G50 (60J20 68M10)",
  MRnumber =     "MR2522878",
  bibdate =      "Wed May 5 19:28:04 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1243430608",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: article
filename: pagerank.bib
label: Fortunato:2007:RWD
author: Santo Fortunato and Alessandro Flammini
title: Random walks on directed networks: the case of PageRank
journal: j-INT-J-BIFURC-CHAOS-APPL-SCI-ENG
volume: 17
number: 7
pages: 2343--2353
year: 2007
CODEN: IJBEE4
DOI: https://doi.org/10.1142/S0218127407018439
ISSN: 0218-1274 OR 02181274
MRclass: 60G50 (05C38 68M10); 60G50 05C38 68M10 82B41
MRnumber: MR2349743 (2008h:60171)
bibdate: Wed May 5 19:28:06 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:06 ???
fjournal: International Journal of Bifurcation and Chaos in Applied Sciences and Engineering
ZMnumber: 1142.68311
acknowledgement: ack-nhfb
rawauthor: Santo Fortunato and Alessandro Flammini
rawtitle: Random walks on directed networks: the case of {PageRank}
entry:
@Article{Fortunato:2007:RWD,
  author =       "Santo Fortunato and Alessandro Flammini",
  title =        "Random walks on directed networks: the case of
                 {PageRank}",
  journal =      j-INT-J-BIFURC-CHAOS-APPL-SCI-ENG,
  volume =       "17",
  number =       "7",
  pages =        "2343--2353",
  year =         "2007",
  CODEN =        "IJBEE4",
  DOI =          "https://doi.org/10.1142/S0218127407018439",
  ISSN =         "0218-1274",
  MRclass =      "60G50 (05C38 68M10); 60G50 05C38 68M10 82B41",
  MRnumber =     "MR2349743 (2008h:60171)",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  ZMnumber =     "1142.68311",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Bifurcation and Chaos in
                 Applied Sciences and Engineering",
}


bibtype: article
filename: pagerank.bib
label: Gleich:2007:APP
author: D. F. Gleich and M. Polito
title: Approximating personalized PageRank with minimal use of webgraph data
journal: j-INTERNET-MATH
volume: 3
number: 3
pages: 257--294
year: 2007
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
bibdate: Tue Aug 11 16:52:54 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:52:54 ???
URL: http://projecteuclid.org/euclid.im/1204906158
fjournal: Internet Mathematics
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: D. F. Gleich and M. Polito
rawtitle: Approximating personalized {PageRank} with minimal use of webgraph data
entry:
@Article{Gleich:2007:APP,
  author =       "D. F. Gleich and M. Polito",
  title =        "Approximating personalized {PageRank} with minimal use
                 of webgraph data",
  journal =      j-INTERNET-MATH,
  volume =       "3",
  number =       "3",
  pages =        "257--294",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  bibdate =      "Tue Aug 11 16:52:54 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/euclid.im/1204906158",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: incollection
filename: pagerank.bib
label: Gleich:2007:TRP
author: David Gleich and Peter Glynn and Gene Golub and Chen Greif
editor: A. Frommer and M. W. Mahoney and D. B. Szyld
booktitle: Internationales Begegnungs- und Forschungszentrum fur Informatik (IBFI), Schloss Dagstuhl, Germany
title: Three results on the PageRank vector: eigenstructure, sensitivity, and the derivative
publisher: International Begegnungs- und Forschungszentrum fur Informatik
address: Wadern, Germany
year: 2007
bibdate: Fri Jun 3 10:03:23 MDT 2011
bibsource: fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2011.06.03 10:03:23 MDT
series: Dagstuhl seminar proceedings 07071
URL: http://drops.dagstuhl.de/opus/volltexte/2007/1061/pdf/07071.GleichDavid.Paper.1061
acknowledgement: ack-nhfb
rawauthor: David Gleich and Peter Glynn and Gene Golub and Chen Greif
raweditor: A. Frommer and M. W. Mahoney and D. B. Szyld
rawtitle: Three results on the {PageRank} vector: eigenstructure, sensitivity, and the derivative
rawbooktitle:
{Internationales Begegnungs- und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany}

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


bibtype: inproceedings
filename: pagerank.bib
label: Gori:2007:IRW
author: Marco Gori and Augusto Pucci
editor: Manuela M. Veloso
title: ItemRank: A random-walk based scoring algorithm for recommender engines
publisher: AAAI Press
address: Menlo Park, CA, USA
pages: 2766--2771
year: 2007
ISBN: 1-57735-298-X OR 157735298X
ISBN13: 978-1-57735-298-3 OR 9781577352983
LCCN: Q335.5 .I55 2007
bibdate: Tue Aug 11 16:56:21 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 16:56:21 ???
URL: http://ijcai.org/papers07/Papers/IJCAI07-444.pdf
acknowledgement: ack-nhfb
bookpages: xlvi + 2954 (two volumes)
rawauthor: Marco Gori and Augusto Pucci
raweditor: Manuela M. Veloso
rawtitle: {ItemRank}: A random-walk based scoring algorithm for recommender engines
booktitle:
IJCAI--07, proceedings of the Twentieth International Joint Conference on Artificial Intelligence: Hyderabad, India, 6-12 January, 2007

rawbooktitle:
{IJCAI--07, proceedings of the Twentieth International Joint Conference on Artificial Intelligence: Hyderabad, India, 6-12 January, 2007}

entry:
@InProceedings{Gori:2007:IRW,
  author =       "Marco Gori and Augusto Pucci",
  editor =       "Manuela M. Veloso",
  booktitle =    "{IJCAI--07, proceedings of the Twentieth International
                 Joint Conference on Artificial Intelligence: Hyderabad,
                 India, 6-12 January, 2007}",
  title =        "{ItemRank}: A random-walk based scoring algorithm for
                 recommender engines",
  publisher =    "AAAI Press",
  address =      "Menlo Park, CA, USA",
  pages =        "2766--2771",
  year =         "2007",
  ISBN =         "1-57735-298-X",
  ISBN-13 =      "978-1-57735-298-3",
  LCCN =         "Q335.5 .I55 2007",
  bibdate =      "Tue Aug 11 16:56:21 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ijcai.org/papers07/Papers/IJCAI07-444.pdf",
  acknowledgement = ack-nhfb,
  bookpages =    "xlvi + 2954 (two volumes)",
  xxaddress =    pub-MORGAN-KAUFMANN:adr,
  xxbooktitle =  "Proceedings of the 20th International Joint Conference
                 on Artificial Intelligence, IJCAI'07, San Francisco,
                 CA",
  xxpublisher =  pub-MORGAN-KAUFMANN,
}


bibtype: inbook
filename: pagerank.bib
label: Gray:2007:IOS
author: Andrew P. Gray and Chen Greif and Tracy Lau
title: An inner, outer stationary iteration for computing PageRank
volume: 07071
publisher: International Begegnungs- und Forschungszentrum fur Informatik
address: Wadern, Germany
year: 2007
bibdate: Fri Feb 19 15:32:30 2010
bibsource: fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 15:32:30 ???
series: Dagstuhl seminar proceedings
URL: http://drops.dagstuhl.de/opus/volltexte/2007/1062/pdf/07071.GreifChen.Paper.1062
acknowledgement: ack-nhfb
rawauthor: Andrew P. Gray and Chen Greif and Tracy Lau
rawtitle: An inner, outer stationary iteration for computing {PageRank}
entry:
@InBook{Gray:2007:IOS,
  author =       "Andrew P. Gray and Chen Greif and Tracy Lau",
  title =        "An inner, outer stationary iteration for computing
                 {PageRank}",
  volume =       "07071",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "????",
  year =         "2007",
  ISBN =         "????",
  ISBN-13 =      "????",
  LCCN =         "????",
  bibdate =      "Fri Feb 19 15:32:30 2010",
  bibsource =    "fsz3950.oclc.org:210/WorldCat;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2007/1062/pdf/07071.GreifChen.Paper.1062",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Guo:2007:MAC
author: Ye Guo
title: MixPR --- An Approach of Combining Content and Links of Web Page[s]
crossref: Lei:2007:FPF
volume: 2
pages: 456--460
year: 2007
DOI: https://doi.org/10.1109/FSKD.2007.407
bibdate: Thu May 06 15:23:46 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:23:46 ???
acknowledgement: ack-nhfb
rawauthor: Ye Guo
rawtitle: {MixPR} --- An Approach of Combining Content and Links of {Web} Page[s]
entry:
@InProceedings{Guo:2007:MAC,
  author =       "Ye Guo",
  title =        "{MixPR} --- An Approach of Combining Content and Links
                 of {Web} Page[s]",
  crossref =     "Lei:2007:FPF",
  volume =       "2",
  pages =        "456--460",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/FSKD.2007.407",
  bibdate =      "Thu May 06 15:23:46 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Guo:2007:PPW
author: Yong Zhen Guo and Kotagiri Ramamohanarao and Laurence A. F. Park
booktitle: IEEE / WIC / ACM International Conference on Web Intelligence
title: Personalized PageRank for Web Page Prediction Based on Access Time-Length and Frequency
crossref: Lin:2007:PIW
pages: 687--690
year: 2007
DOI: https://doi.org/10.1109/WI.2007.58
ISBN: 0-7695-3026-5 OR 0769530265
ISBN13: 978-0-7695-3026-0 OR 9780769530260
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=4427174
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4427043
rawauthor: Yong Zhen Guo and Kotagiri Ramamohanarao and Laurence A. F. Park
rawbooktitle: {IEEE\slash WIC\slash ACM International Conference on Web Intelligence}
rawtitle: Personalized {PageRank} for {Web} Page Prediction Based on Access Time-Length and Frequency
abstract:
Web page prefetching techniques are used to address the access latency problem of the Internet. To perform successful prefetching, we must be able to predict the next set of pages that will be accessed by users. The PageRank algorithm used by Google is able to compute the popularity of a set of Web pages based on their link structure. In this paper, a novel PageRank-like algorithm is proposed for conducting Web page prediction. Two biasing factors are adopted to personalize PageRank, so that it favors the pages that are more important to users. One factor is the length of time spent on visiting a page and the other is the frequency that a page was visited. The experiments conducted show that using these two factors simultaneously to bias PageRank results in more accurate Web page prediction.

rawabstract:
Web page prefetching techniques are used to address the access latency problem of the Internet. To perform successful prefetching, we must be able to predict the next set of pages that will be accessed by users. The PageRank algorithm used by Google is able to compute the popularity of a set of Web pages based on their link structure. In this paper, a novel PageRank-like algorithm is proposed for conducting Web page prediction. Two biasing factors are adopted to personalize PageRank, so that it favors the pages that are more important to users. One factor is the length of time spent on visiting a page and the other is the frequency that a page was visited. The experiments conducted show that using these two factors simultaneously to bias PageRank results in more accurate Web page prediction.

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


bibtype: article
filename: pagerank.bib
label: He:2007:CSW
author: Xiaofei He and Deng Cai and Ji-Rong Wen and Wei-Ying Ma and Hong-Jiang Zhang
title: Clustering and searching WWW images using link and page layout analysis
journal: j-TOMCCAP
volume: 3
number: 2
pages: 10:1--10:??
month: may
monthnumber: 5
year: 2007
DOI: https://doi.org/10.1145/1230812.1230816
ISSN: 1551-6857 (print), 1551-6865 (electronic) OR 15516857 (print), 15516865 (electronic)
ISSNL: 1551-6857 OR 15516857
bibdate: Mon Jun 16 17:10:04 MDT 2008
bibtimestamp: 2008.06.16 17:10:04 MDT
URL: https://www.math.utah.edu/pub/tex/bib/tomccap.bib
fjournal: ACM Transactions on Multimedia Computing, Communications, and Applications
keywords: image clustering; image search; link analysis; Web mining
acknowledgement: ack-nhfb
articleno: 10
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J961
rawauthor: Xiaofei He and Deng Cai and Ji-Rong Wen and Wei-Ying Ma and Hong-Jiang Zhang
rawtitle: Clustering and searching {WWW} images using link and page layout analysis
bibsource:
http://www.acm.org/pubs/contents/journals/tomccap/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

abstract:
Due to the rapid growth of the number of digital images on the Web, there is an increasing demand for an effective and efficient method for organizing and retrieving the available images. This article describes iFind, a system for clustering and searching WWW images. By using a vision-based page segmentation algorithm, a Web page is partitioned into blocks, and the textual and link information of an image can be accurately extracted from the block containing that image. The textual information is used for image indexing. By extracting the page-to-block, block-to-image, block-to-page relationships through link structure and page layout analysis, we construct an image graph. Our method is less sensitive to noisy links than previous methods like PageRank, HITS, and PicASHOW, and hence the image graph can better reflect the semantic relationship between images. Using the notion of Markov Chain, we can compute the limiting probability distributions of the images, ImageRanks, which characterize the importance of the images. The ImageRanks are combined with the relevance scores to produce the final ranking for image search. With the graph models, we can also use techniques from spectral graph theory for image clustering and embedding, or 2-D visualization. Some experimental results on 11.6 million images downloaded from the Web are provided in the article.

rawabstract:
Due to the rapid growth of the number of digital images on the Web, there is an increasing demand for an effective and efficient method for organizing and retrieving the available images. This article describes iFind, a system for clustering and searching WWW images. By using a vision-based page segmentation algorithm, a Web page is partitioned into blocks, and the textual and link information of an image can be accurately extracted from the block containing that image. The textual information is used for image indexing. By extracting the page-to-block, block-to-image, block-to-page relationships through link structure and page layout analysis, we construct an image graph. Our method is less sensitive to noisy links than previous methods like PageRank, HITS, and PicASHOW, and hence the image graph can better reflect the semantic relationship between images. Using the notion of Markov Chain, we can compute the limiting probability distributions of the images, ImageRanks, which characterize the importance of the images. The ImageRanks are combined with the relevance scores to produce the final ranking for image search. With the graph models, we can also use techniques from spectral graph theory for image clustering and embedding, or 2-D visualization. Some experimental results on 11.6 million images downloaded from the Web are provided in the article.

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


bibtype: article
filename: pagerank.bib
label: Horn:2007:GSP
author: Roger A. Horn and Stefano Serra-Capizzano
title: A general setting for the parametric Google matrix
journal: j-INTERNET-MATH
volume: 3
number: 4
pages: 385--411
year: 2007
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
bibdate: Tue Aug 11 17:04:27 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:04:27 ???
URL: http://projecteuclid.org/euclid.im/1227025007
fjournal: Internet Mathematics
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Roger A. Horn and Stefano Serra-Capizzano
rawtitle: A general setting for the parametric {Google} matrix
entry:
@Article{Horn:2007:GSP,
  author =       "Roger A. Horn and Stefano Serra-Capizzano",
  title =        "A general setting for the parametric {Google} matrix",
  journal =      j-INTERNET-MATH,
  volume =       "3",
  number =       "4",
  pages =        "385--411",
  month =        "????",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  bibdate =      "Tue Aug 11 17:04:27 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/euclid.im/1227025007",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: inproceedings
filename: pagerank.bib
label: Hussain:2007:SARa
author: F. K. Hussain and E. Chang and O. K. Hussain
title: State of the art review of the existing PageRank based algorithms for trust computation
crossref: Dini:2007:SIC
pages: 75--75
year: 2007
DOI: https://doi.org/10.1109/ICSNC.2007.78
bibdate: Thu May 06 15:25:50 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:25:50 ???
acknowledgement: ack-nhfb
rawauthor: F. K. Hussain and E. Chang and O. K. Hussain
rawtitle: State of the art review of the existing {PageRank} based algorithms for trust computation
abstract:
In this paper we present a state of the art review of PageRank based approaches for trust and reputation computation. We divide the approaches that make use of PageRank method for trust and reputation computation, into six different classes. Each of the six classes is discussed in this paper.

rawabstract:
In this paper we present a state of the art review of PageRank based approaches for trust and reputation computation. We divide the approaches that make use of PageRank method for trust and reputation computation, into six different classes. Each of the six classes is discussed in this paper.

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


bibtype: inproceedings
filename: pagerank.bib
label: Hussain:2007:SARb
author: F. K. Hussain and E. Chang and O. K. Hussain
crossref: Ramakrishnan:2007:PSI
pages: 43--43
year: 2007
DOI: https://doi.org/10.1109/ICIMP.2007.44
bibdate: Thu May 06 16:11:46 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:11:46 ???
acknowledgement: ack-nhfb
rawauthor: F. K. Hussain and E. Chang and O. K. Hussain
title:
State of the art review of the existing PageRank based algorithms for trust and reputation computation

rawtitle:
State of the art review of the existing {PageRank} based algorithms for trust and reputation computation

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


bibtype: article
filename: pagerank.bib
label: Ipsen:2007:PCS
author: Ilse C. F. Ipsen and Teresa M. Selee
title: PageRank computation, with special attention to dangling nodes
journal: j-SIAM-J-MAT-ANA-APPL
volume: 29
number: 4
pages: 1281--1296
month: nov
monthnumber: 11
year: 2007
CODEN: SJMAEL
DOI: https://doi.org/10.1137/060664331
ISSN: 0895-4798 (print), 1095-7162 (electronic) OR 08954798 (print), 10957162 (electronic)
ISSNL: 0895-4798 OR 08954798
MRclass: 65C40 (15A06 15A18 68M10 68P20)
MRnumber: MR2369296 (2009a:65013)
bibdate: Wed May 5 19:28:06 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:06 ???
fjournal: SIAM Journal on Matrix Analysis and Applications
ZMnumber: 1156.65038
acknowledgement: ack-nhfb
journalURL: http://epubs.siam.org/simax
rawauthor: Ilse C. F. Ipsen and Teresa M. Selee
rawtitle: {PageRank} computation, with special attention to dangling nodes
abstract:
We present a simple algorithm for computing the PageRank (stationary distribution) of the stochastic Google matrix $G$. The algorithm lumps all dangling nodes into a single node. We express lumping as a similarity transformation of $G$ and show that the PageRank of the nondangling nodes can be computed separately from that of the dangling nodes. The algorithm applies the power method only to the smaller lumped matrix, but the convergence rate is the same as that of the power method applied to the full matrix $G$. The efficiency of the algorithm increases as the number of dangling nodes increases. We also extend the expression for PageRank and the algorithm to more general Google matrices that have several different dangling node vectors, when it is required to distinguish among different classes of dangling nodes. We also analyze the effect of the dangling node vector on the PageRank and show that the PageRank of the dangling nodes depends strongly on that of the nondangling nodes but not vice versa. Last we present a Jordan decomposition of the Google matrix for the (theoretical) extreme case when all Web pages are dangling nodes.

keywords:
Google; Jordan decomposition; lumping; power method; rank-one matrix; similarity transformation; stationary distribution; stochastic matrix

rawabstract:
We present a simple algorithm for computing the PageRank (stationary distribution) of the stochastic Google matrix $G$. The algorithm lumps all dangling nodes into a single node. We express lumping as a similarity transformation of $G$ and show that the PageRank of the nondangling nodes can be computed separately from that of the dangling nodes. The algorithm applies the power method only to the smaller lumped matrix, but the convergence rate is the same as that of the power method applied to the full matrix $G$. The efficiency of the algorithm increases as the number of dangling nodes increases. We also extend the expression for PageRank and the algorithm to more general Google matrices that have several different dangling node vectors, when it is required to distinguish among different classes of dangling nodes. We also analyze the effect of the dangling node vector on the PageRank and show that the PageRank of the dangling nodes depends strongly on that of the nondangling nodes but not vice versa. Last we present a Jordan decomposition of the Google matrix for the (theoretical) extreme case when all Web pages are dangling nodes.

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


bibtype: inproceedings
filename: pagerank.bib
label: Jiang:2007:SBC
author: Qiancheng Jiang and Yan Zhang
title: SiteRank-Based Crawling Ordering Strategy for Search Engines
crossref: Miyazaki:2007:CPI
pages: 259--263
year: 2007
DOI: https://doi.org/10.1109/CIT.2007.35
bibdate: Thu May 06 15:48:26 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:48:26 ???
acknowledgement: ack-nhfb
rawauthor: Qiancheng Jiang and Yan Zhang
rawtitle: {SiteRank}-Based Crawling Ordering Strategy for Search Engines
entry:
@InProceedings{Jiang:2007:SBC,
  author =       "Qiancheng Jiang and Yan Zhang",
  title =        "{SiteRank}-Based Crawling Ordering Strategy for Search
                 Engines",
  crossref =     "Miyazaki:2007:CPI",
  pages =        "259--263",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/CIT.2007.35",
  bibdate =      "Thu May 06 15:48:26 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Kao:2007:FPC
author: Hung-Yu Kao and Seng-Feng Lin
booktitle: IEEE / WIC / ACM International Conference on Web Intelligence
title: A Fast PageRank Convergence Method based on the Cluster Prediction
crossref: Lin:2007:PIW
pages: 593--599
year: 2007
DOI: https://doi.org/10.1109/WI.2007.129
ISBN: 0-7695-3026-5 OR 0769530265
ISBN13: 978-0-7695-3026-0 OR 9780769530260
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=4427158
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4427043
rawauthor: Hung-Yu Kao and Seng-Feng Lin
rawbooktitle: {IEEE\slash WIC\slash ACM International Conference on Web Intelligence}
rawtitle: A Fast {PageRank} Convergence Method based on the Cluster Prediction
abstract:
In recent years, search engines have already played the key roles among Web applications, and link analysis algorithms are the major methods to measure the important values of Web pages. These algorithms employ the conventional flat Web graph built by Web pages and link relations of Web pages to obtain the relative importance of Web objects. Previous researches have observed that PageRank-like link analysis algorithms have a bias against newly created Web pages. A new ranking algorithm called Page Quality was then proposed to solve this issue. Page Quality predicates future ranking values by the difference rate between the current ranking value and the previous ranking value. In this paper, we propose a new algorithm called DRank to diminish the bias of PageRank-like link analysis algorithms, and attain the better performance than Page Quality. In this algorithm, we model Web graph as a three-layer graph which includes Host Graph, Directory Graph and Page Graph by using the hierarchical structure of URLs and the structure of link relation of Web pages. We calculate the importance of Hosts, Directories and Pages by weighted graph we built and then the clustering distribution of PageRank values of pages within directories is observed. We can then predicate the more accurate values of page importance to diminish the bias of newly created pages by the clustering characteristic of PageRank. Experiment results show that DRank algorithm works well on predicating future ranking values of pages and outperform Page Quality.

rawabstract:
In recent years, search engines have already played the key roles among Web applications, and link analysis algorithms are the major methods to measure the important values of Web pages. These algorithms employ the conventional flat Web graph built by Web pages and link relations of Web pages to obtain the relative importance of Web objects. Previous researches have observed that PageRank-like link analysis algorithms have a bias against newly created Web pages. A new ranking algorithm called Page Quality was then proposed to solve this issue. Page Quality predicates future ranking values by the difference rate between the current ranking value and the previous ranking value. In this paper, we propose a new algorithm called DRank to diminish the bias of PageRank-like link analysis algorithms, and attain the better performance than Page Quality. In this algorithm, we model Web graph as a three-layer graph which includes Host Graph, Directory Graph and Page Graph by using the hierarchical structure of URLs and the structure of link relation of Web pages. We calculate the importance of Hosts, Directories and Pages by weighted graph we built and then the clustering distribution of PageRank values of pages within directories is observed. We can then predicate the more accurate values of page importance to diminish the bias of newly created pages by the clustering characteristic of PageRank. Experiment results show that DRank algorithm works well on predicating future ranking values of pages and outperform Page Quality.

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


bibtype: inproceedings
filename: pagerank.bib
label: Kohlschutter:2007:UAT
author: Christian Kohlschutter and Paul-Alexandru Chirita and Wolfgang Nejdl
editor: ACM
title: Utility analysis for topically biased PageRank
publisher: pub-ACM
address: pub-ACM:adr
pages: 1211--1212
year: 2007
DOI: https://doi.org/10.1145/511446.511513
ISBN: 1-59593-654-8 OR 1595936548
ISBN13: 978-1-59593-654-7 OR 9781595936547
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
keywords: biased PageRank; open directory; personalized search
acknowledgement: ack-nhfb
rawauthor: Christian Kohlsch{\"u}tter and Paul-Alexandru Chirita and Wolfgang Nejdl
raweditor: {ACM}
rawtitle: Utility analysis for topically biased {PageRank}
booktitle:
International World Wide Web Conference Proceedings of the 16th international conference on World Wide Web

abstract:
PageRank is known to be an efficient metric for computing general document importance in the Web. While commonly used as a one-size-fits-all measure, the ability to produce topically biased ranks has not yet been fully explored in detail. In particular, it was still unclear to what granularity of 'topic' the computation of biased page ranks makes sense. In this paper we present the results of a thorough quantitative and qualitative analysis of biasing PageRank on Open Directory categories. We show that the MAP quality of Biased PageRank generally increases with the ODP level up to a certain point, thus sustaining the usage of more specialized categories to bias PageRank on, in order to improve topic specific search.

rawabstract:
PageRank is known to be an efficient metric for computing general document importance in the Web. While commonly used as a one-size-fits-all measure, the ability to produce topically biased ranks has not yet been fully explored in detail. In particular, it was still unclear to what granularity of 'topic' the computation of biased page ranks makes sense. In this paper we present the results of a thorough quantitative and qualitative analysis of biasing PageRank on Open Directory categories. We show that the MAP quality of Biased PageRank generally increases with the ODP level up to a certain point, thus sustaining the usage of more specialized categories to bias PageRank on, in order to improve topic specific search.

rawbooktitle:
International World Wide Web Conference Proceedings of the 16th international conference on World Wide Web

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


bibtype: inbook
filename: pagerank.bib
label: Kollias:2007:APC
author: Giorgos Kollias and Efstratios Gallopoulos
title: Asynchronous PageRank computation in an interactive multithreading environment
volume: 07071
publisher: International Begegnungs- und Forschungszentrum fur Informatik
address: Wadern, Germany
year: 2007
bibdate: Fri Feb 19 15:32:30 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 15:32:30 ???
series: Dagstuhl seminar proceedings
URL: http://drops.dagstuhl.de/opus/volltexte/2007/1065/pdf/07071.KolliasGiorgios.Paper.1065
acknowledgement: ack-nhfb
rawauthor: Giorgos Kollias and Efstratios Gallopoulos
rawtitle: Asynchronous {PageRank} computation in an interactive multithreading environment
entry:
@InBook{Kollias:2007:APC,
  author =       "Giorgos Kollias and Efstratios Gallopoulos",
  title =        "Asynchronous {PageRank} computation in an interactive
                 multithreading environment",
  volume =       "07071",
  publisher =    "International Begegnungs- und Forschungszentrum
                 f{\"u}r Informatik",
  address =      "Wadern, Germany",
  pages =        "????",
  year =         "2007",
  ISBN =         "????",
  ISBN-13 =      "????",
  bibdate =      "Fri Feb 19 15:32:30 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       "Dagstuhl seminar proceedings",
  URL =          "http://drops.dagstuhl.de/opus/volltexte/2007/1065/pdf/07071.KolliasGiorgios.Paper.1065",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: Lee:2007:TSA
author: Chris P. Lee and Gene H. Golub and Stefanos A. Zenios
title: A two-stage algorithm for computing PageRank and multistage generalizations
journal: j-INTERNET-MATH
volume: 4
number: 4
pages: 299--327
year: 2007
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 68M11
MRnumber: MR2522947
bibdate: Wed May 5 19:28:06 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:06 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1243430809
fjournal: Internet Mathematics
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: Chris P. Lee and Gene H. Golub and Stefanos A. Zenios
rawtitle: A two-stage algorithm for computing {PageRank} and multistage generalizations
abstract:
The PageRank model pioneered by Google is the most common approach for generating web search results. We present a two-stage algorithm for computing the PageRank vector where the algorithm exploits the lumpability of the underlying Markov chain. We make three contributions. First, the algorithm speeds up the PageRank calculation significantly. With web graphs having millions of webpages, the speed-up is typically in the two- to three-fold range. The algorithm can also embed other acceleration methods such as quadratic extrapolation, the Gauss-Seidel method, or the Biconjugate gradient stable method for an even greater speed-up; cumulative speed-up is as high as 7 to 14 times. The second contribution relates to the handling of dangling nodes. Conventionally, dangling nodes are included only towards the end of the computation. While this approach works reasonably well, it can fail in extreme cases involving aggressive personalization. We prove that our algorithm is the generally correct way of handling dangling nodes using probabilistic arguments. We also discuss variants of our algorithm, including a multistage extension for calculating a generalized version of the PageRank model where different personalization vectors are used for webpages of different classes. The ability to form class associations may be useful for building more refined models of web traffic.

rawabstract:
The PageRank model pioneered by Google is the most common approach for generating web search results. We present a two-stage algorithm for computing the PageRank vector where the algorithm exploits the lumpability of the underlying Markov chain. We make three contributions. First, the algorithm speeds up the PageRank calculation significantly. With web graphs having millions of webpages, the speed-up is typically in the two- to three-fold range. The algorithm can also embed other acceleration methods such as quadratic extrapolation, the Gauss-Seidel method, or the Biconjugate gradient stable method for an even greater speed-up; cumulative speed-up is as high as 7 to 14 times. The second contribution relates to the handling of dangling nodes. Conventionally, dangling nodes are included only towards the end of the computation. While this approach works reasonably well, it can fail in extreme cases involving aggressive personalization. We prove that our algorithm is the generally correct way of handling dangling nodes using probabilistic arguments. We also discuss variants of our algorithm, including a multistage extension for calculating a generalized version of the PageRank model where different personalization vectors are used for webpages of different classes. The ability to form class associations may be useful for building more refined models of web traffic.

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


bibtype: inproceedings
filename: pagerank.bib
label: Li:2007:HCN
author: Cun-he Li and Ke-qiang Lu
title: Hyperlink Classification: a New Approach to Improve PageRank
crossref: Tjoa:2007:DIC
pages: 274--277
year: 2007
DOI: https://doi.org/10.1109/DEXA.2007.14
ISBN: 0-7695-2932-1, 0-7695-2932-1 OR 0769529321, 0769529321
ISBN13: 978-0-7695-2932-5, 978-0-7695-2932-5 OR 9780769529325, 9780769529325
LCCN: QA76.9.D3
bibdate: Fri Feb 19 18:23:12 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.02.19 18:23:12 ???
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4312900
acknowledgement: ack-nhfb
bookURL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4312838
rawauthor: Cun-he Li and Ke-qiang Lu
rawtitle: Hyperlink Classification: a New Approach to Improve {PageRank}
entry:
@InProceedings{Li:2007:HCN,
  author =       "Cun-he Li and Ke-qiang Lu",
  title =        "Hyperlink Classification: a New Approach to Improve
                 {PageRank}",
  crossref =     "Tjoa:2007:DIC",
  pages =        "274--277",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/DEXA.2007.14",
  ISBN =         "0-7695-2932-1, 0-7695-2932-1",
  ISBN-13 =      "978-0-7695-2932-5, 978-0-7695-2932-5",
  LCCN =         "QA76.9.D3",
  bibdate =      "Fri Feb 19 18:23:12 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4312900",
  acknowledgement = ack-nhfb,
  book-URL =     "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4312838",
}


bibtype: article
filename: pagerank.bib
label: Litvak:2007:DPW
author: N. Litvak and W. R. W. Scheinhardt and Y. Volkovich
title: In-Degree and PageRank: why do they follow similar power laws?
journal: j-INTERNET-MATH
volume: 4
number: 2--3
pages: 175--198
year: 2007
ISSN: 1542-7951 (print), 1944-9488 (electronic) OR 15427951 (print), 19449488 (electronic)
ISSNL: 1542-7951 OR 15427951
MRclass: 62H99 (62E15 62E17 62P99 68M10)
MRnumber: MR2522875 (2010f:62177)
MRreviewer: Pranesh Kumar
bibdate: Wed May 5 19:28:06 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:06 ???
URL: http://projecteuclid.org/getRecord?id=euclid.im/1243430605
fjournal: Internet Mathematics
acknowledgement: ack-nhfb
journalURL: http://projecteuclid.org/info/euclid.im
rawauthor: N. Litvak and W. R. W. Scheinhardt and Y. Volkovich
rawtitle: {In-Degree} and {PageRank}: why do they follow similar power laws?
entry:
@Article{Litvak:2007:DPW,
  author =       "N. Litvak and W. R. W. Scheinhardt and Y. Volkovich",
  title =        "{In-Degree} and {PageRank}: why do they follow similar
                 power laws?",
  journal =      j-INTERNET-MATH,
  volume =       "4",
  number =       "2--3",
  pages =        "175--198",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1542-7951 (print), 1944-9488 (electronic)",
  ISSN-L =       "1542-7951",
  MRclass =      "62H99 (62E15 62E17 62P99 68M10)",
  MRnumber =     "MR2522875 (2010f:62177)",
  MRreviewer =   "Pranesh Kumar",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://projecteuclid.org/getRecord?id=euclid.im/1243430605",
  acknowledgement = ack-nhfb,
  fjournal =     "Internet Mathematics",
  journal-URL =  "http://projecteuclid.org/info/euclid.im",
}


bibtype: inproceedings
filename: pagerank.bib
label: Liu:2007:EBE
author: Maofu Liu and Wenjie Li and Mingli Wu and Hujun Hu
crossref: Ock:2007:ASI
pages: 117--122
year: 2007
DOI: https://doi.org/10.1109/ALPIT.2007.9
bibdate: Thu May 06 16:49:34 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:49:34 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: Maofu Liu and Wenjie Li and Mingli Wu and Hujun Hu
title:
Event-Based Extractive Summarization Using Event Semantic Relevance from External Linguistic Resource

rawtitle:
Event-Based Extractive Summarization Using Event Semantic Relevance from External Linguistic Resource

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


bibtype: inproceedings
filename: pagerank.bib
label: Liu:2007:KEU
author: Jianyi Liu and Jinghua Wang
title: Keyword Extraction Using Language Network
crossref: IEEE:2007:ICN
pages: 129--134
year: 2007
DOI: https://doi.org/10.1109/NLPKE.2007.4368023
bibdate: Thu May 06 15:29:51 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:29:51 ???
acknowledgement: ack-nhfb
rawauthor: Jianyi Liu and Jinghua Wang
rawtitle: Keyword Extraction Using Language Network
entry:
@InProceedings{Liu:2007:KEU,
  author =       "Jianyi Liu and Jinghua Wang",
  title =        "Keyword Extraction Using Language Network",
  crossref =     "IEEE:2007:ICN",
  pages =        "129--134",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/NLPKE.2007.4368023",
  bibdate =      "Thu May 06 15:29:51 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Mason:2007:WMF
author: Zachary Mason
title: WordRank: a Method for Finding Search-Ad Keywords for Internet Merchants
crossref: Clifton:2006:SIC
pages: 12--12
year: 2007
DOI: https://doi.org/10.1109/ICIW.2007.73
bibdate: Thu May 06 16:27:14 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:27:14 ???
acknowledgement: ack-nhfb
rawauthor: Zachary Mason
rawtitle: {WordRank}: a Method for Finding Search-Ad Keywords for {Internet} Merchants
entry:
@InProceedings{Mason:2007:WMF,
  author =       "Zachary Mason",
  title =        "{WordRank}: a Method for Finding Search-Ad Keywords
                 for {Internet} Merchants",
  crossref =     "Clifton:2006:SIC",
  pages =        "12--12",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/ICIW.2007.73",
  bibdate =      "Thu May 06 16:27:14 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Melucci:2007:PWO
author: Massimo Melucci and Luca Pretto
editor: Giambattista Amati and Claudio Carpineto and Giovanni Romano
title: PageRank: when order changes
publisher: pub-SV
address: pub-SV:adr
pages: 581--588
year: 2007
DOI: https://doi.org/10.1145/1060745.1060827
ISBN: 3-540-71494-4 OR 3540714944
ISBN13: 978-3-540-71494-1 OR 9783540714941
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
bibdate: Sat May 8 18:33:10 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:10 MDT
series: ser-LNCS
acknowledgement: ack-nhfb
rawauthor: Massimo Melucci and Luca Pretto
raweditor: Giambattista Amati and Claudio Carpineto and Giovanni Romano
rawtitle: {PageRank}: when order changes
booktitle:
Advances in information retrieval: 29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007: proceedings

abstract:
As PageRank is a ranking algorithm, it is of prime interest to study the order induced by its values on webpages. In this paper a thorough mathematical analysis of PageRank-induced order changes when the damping factor varies is provided. Conditions that do not allow variations in the order are studied, and the mechanisms that make the order change are mathematically investigated. Moreover the influence on the order of a truncation in the actual computation of PageRank through a power series is analysed. Experiments carried out on a large Web digraph to integrate the mathematical analysis show that PageRank -- while working on a real digraph -- tends to hinder variations in the order of large rankings, presenting a high stability in its induced order both in the face of large variations of the damping factor value and in the face of truncations in its computation.

rawabstract:
As PageRank is a ranking algorithm, it is of prime interest to study the order induced by its values on webpages. In this paper a thorough mathematical analysis of PageRank-induced order changes when the damping factor varies is provided. Conditions that do not allow variations in the order are studied, and the mechanisms that make the order change are mathematically investigated. Moreover the influence on the order of a truncation in the actual computation of PageRank through a power series is analysed. Experiments carried out on a large Web digraph to integrate the mathematical analysis show that PageRank -- while working on a real digraph -- tends to hinder variations in the order of large rankings, presenting a high stability in its induced order both in the face of large variations of the damping factor value and in the face of truncations in its computation.

rawbooktitle:
{Advances in information retrieval: 29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007: proceedings}

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


bibtype: inproceedings
filename: pagerank.bib
label: Mousavi:2007:CWU
author: H. Mousavi and M. E. Rafiei and A. Movaghar
title: Characterizing the Web Using a New Uniform Sampling Approach
crossref: IEEE:2007:ICC
pages: 1--5
year: 2007
DOI: https://doi.org/10.1109/COMSWA.2007.382558
bibdate: Thu May 06 16:46:35 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:46:35 ???
acknowledgement: ack-nhfb
rawauthor: H. Mousavi and M. E. Rafiei and A. Movaghar
rawtitle: Characterizing the {Web} Using a New Uniform Sampling Approach
entry:
@InProceedings{Mousavi:2007:CWU,
  author =       "H. Mousavi and M. E. Rafiei and A. Movaghar",
  title =        "Characterizing the {Web} Using a New Uniform Sampling
                 Approach",
  crossref =     "IEEE:2007:ICC",
  pages =        "1--5",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/COMSWA.2007.382558",
  bibdate =      "Thu May 06 16:46:35 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Najork:2007:HWH
author: Marc A. Najork and Hugo Zaragoza and Michael J. Taylor
editor: Wessel Kraaij and Arjen P. de Vries
title: HITS on the web: How does it compare?
publisher: pub-ACM
address: pub-ACM:adr
pages: 471--478
year: 2007
DOI: https://doi.org/10.1145/1277741.1277823
ISBN: 1-59593-597-5 OR 1595935975
ISBN13: 978-1-59593-597-7 OR 9781595935977
LCCN: Z699.A1
bibdate: Tue Aug 11 17:30:19 2015
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2015.08.11 17:30:19 ???
acknowledgement: ack-nhfb
bookDOI: https://doi.org/10.1145/1277741
bookpages: 928
rawauthor: Marc A. Najork and Hugo Zaragoza and Michael J. Taylor
raweditor: Wessel Kraaij and Arjen P. de Vries
rawtitle: {HITS} on the web: How does it compare?
booktitle:
Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR2007. Amsterdam (the Netherlands), July 23--27, 2007

rawbooktitle:
{Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR2007. Amsterdam (the Netherlands), July 23--27, 2007}

entry:
@InProceedings{Najork:2007:HWH,
  author =       "Marc A. Najork and Hugo Zaragoza and Michael J.
                 Taylor",
  editor =       "Wessel Kraaij and Arjen P. de Vries",
  booktitle =    "{Proceedings of the 30th Annual International ACM
                 SIGIR Conference on Research and Development in
                 Information Retrieval, SIGIR2007. Amsterdam (the
                 Netherlands), July 23--27, 2007}",
  title =        "{HITS} on the web: How does it compare?",
  publisher =    pub-ACM,
  address =      pub-ACM:adr,
  pages =        "471--478",
  year =         "2007",
  DOI =          "https://doi.org/10.1145/1277741.1277823",
  ISBN =         "1-59593-597-5",
  ISBN-13 =      "978-1-59593-597-7",
  LCCN =         "Z699.A1",
  bibdate =      "Tue Aug 11 17:30:19 2015",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  book-DOI =     "https://doi.org/10.1145/1277741",
  bookpages =    "928",
}


bibtype: inproceedings
filename: pagerank.bib
label: Nakakubo:2007:WPS
author: H. Nakakubo and S. Nakajima and K. Hatano and J. Miyazaki and S. Uemura
title: Web Page Scoring Based on Link Analysis of Web Page Sets
crossref: Tjoa:2007:DIC
pages: 269--273
year: 2007
DOI: https://doi.org/10.1109/DEXA.2007.126
bibdate: Thu May 06 15:51:06 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:51:06 ???
acknowledgement: ack-nhfb
rawauthor: H. Nakakubo and S. Nakajima and K. Hatano and J. Miyazaki and S. Uemura
rawtitle: {Web} Page Scoring Based on Link Analysis of {Web} Page Sets
entry:
@InProceedings{Nakakubo:2007:WPS,
  author =       "H. Nakakubo and S. Nakajima and K. Hatano and J.
                 Miyazaki and S. Uemura",
  title =        "{Web} Page Scoring Based on Link Analysis of {Web}
                 Page Sets",
  crossref =     "Tjoa:2007:DIC",
  pages =        "269--273",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/DEXA.2007.126",
  bibdate =      "Thu May 06 15:51:06 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Nan:2007:ENI
author: He Nan and Gan Wen-yan and Li De Yi
title: Evaluate Nodes Importance in the Network Using Data Field Theory
crossref: Na:2007:IIC
pages: 1225--1234
year: 2007
DOI: https://doi.org/10.1109/ICCIT.2007.88
bibdate: Thu May 06 16:40:05 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 16:40:05 ???
keywords: PageRank
acknowledgement: ack-nhfb
rawauthor: He Nan and Gan Wen-yan and Li De Yi
rawtitle: Evaluate Nodes Importance in the Network Using Data Field Theory
entry:
@InProceedings{Nan:2007:ENI,
  author =       "He Nan and Gan Wen-yan and Li De Yi",
  title =        "Evaluate Nodes Importance in the Network Using Data
                 Field Theory",
  crossref =     "Na:2007:IIC",
  pages =        "1225--1234",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/ICCIT.2007.88",
  bibdate =      "Thu May 06 16:40:05 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank",
}


bibtype: inproceedings
filename: pagerank.bib
label: Nie:2007:CSP
author: Lan Nie and Baoning Wu and Brian D. Davison
editor: ACM
title: A cautious surfer for PageRank
publisher: pub-ACM
address: pub-ACM:adr
pages: 1119--1120
year: 2007
DOI: https://doi.org/10.1145/1149121.1149124
ISBN: 1-59593-654-8 OR 1595936548
ISBN13: 978-1-59593-654-7 OR 9781595936547
bibdate: Sat May 8 18:33:09 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:09 MDT
keywords: authority; ranking performance; spam; trust; web search engine
acknowledgement: ack-nhfb
rawauthor: Lan Nie and Baoning Wu and Brian D. Davison
raweditor: {ACM}
rawtitle: A cautious surfer for {PageRank}
booktitle:
International World Wide Web Conference Proceedings of the 16th international conference on World Wide Web

abstract:
This work proposes a novel cautious surfer to incorporate trust into the process of calculating authority for web pages. We evaluate a total of sixty queries over two large, real-world datasets to demonstrate that incorporating trust can improve PageRank's performance.

rawabstract:
This work proposes a novel cautious surfer to incorporate trust into the process of calculating authority for web pages. We evaluate a total of sixty queries over two large, real-world datasets to demonstrate that incorporating trust can improve PageRank's performance.

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


bibtype: article
filename: pagerank.bib
label: Pedroche:2007:MCP
author: Francisco Pedroche
title: Methods of calculating the PageRank vector
journal: Bol. Soc. Esp. Mat. Apl. S$vec rm e$MA
volume: 39
pages: 7--30
year: 2007
ISSN: 1575-9822 OR 15759822
MRclass: 15A18 (65F10 65F15)
MRnumber: MR2406972 (2009c:15016)
MRreviewer: Juan Manuel Pe{\~n}a
bibdate: Wed May 5 19:28:06 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:06 ???
fjournal: Boleti n de la Sociedad Espanola de Matematica Aplicada. S$vec rm e$MA
acknowledgement: ack-nhfb
rawauthor: Francisco Pedroche
rawtitle: Methods of calculating the {PageRank} vector
entry:
@Article{Pedroche:2007:MCP,
  author =       "Francisco Pedroche",
  title =        "Methods of calculating the {PageRank} vector",
  journal =      "Bol. Soc. Esp. Mat. Apl. S$\vec{\rm e}$MA",
  volume =       "39",
  pages =        "7--30",
  year =         "2007",
  CODEN =        "????",
  ISSN =         "1575-9822",
  MRclass =      "15A18 (65F10 65F15)",
  MRnumber =     "MR2406972 (2009c:15016)",
  MRreviewer =   "Juan Manuel Pe{\~n}a",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Bolet\'\i n de la Sociedad Espa\~nola de Matem\'atica
                 Aplicada. S$\vec{\rm e}$MA",
}


bibtype: inproceedings
filename: pagerank.bib
label: Qiao:2007:EAP
author: Jonathan Qiao and Brittany Jones and Stacy Thrall
editor: Yong Shi and others
booktitle: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
title: An Efficient Algorithm and Its Parallelization for Computing PageRank
volume: 4487--4490
publisher: pub-SV
address: pub-SV:adr
pages: 237--244
year: 2007
DOI: https://doi.org/10.1007/978-3-540-72584-8_31
ISBN: 3-540-72583-0 OR 3540725830
ISBN13: 978-3-540-72583-1 OR 9783540725831
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
bibdate: Sat May 8 18:33:07 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.08 18:33:07 MDT
series: ser-LNCS
keywords: algorithm; dangling nodes; PageRank; power method
acknowledgement: ack-nhfb
rawauthor: Jonathan Qiao and Brittany Jones and Stacy Thrall
rawbooktitle: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
raweditor: Yong Shi and others
rawtitle: An Efficient Algorithm and Its Parallelization for Computing {PageRank}
abstract:
In this paper, an efficient algorithm and its parallelization to compute PageRank are proposed. There are existing algorithms to perform such tasks. However, some algorithms exclude dangling nodes which are an important part and carry important information of the web graph. In this work, we consider dangling nodes as regular web pages without changing the web graph structure and therefore fully preserve the information carried by them. This differs from some other algorithms which include dangling nodes but treat them differently from regular pages for the purpose of efficiency. We then give an efficient algorithm with negligible overhead associated with dangling node treatment. Moreover, the treatment poses little difficulty in the parallelization of the algorithm.

rawabstract:
In this paper, an efficient algorithm and its parallelization to compute PageRank are proposed. There are existing algorithms to perform such tasks. However, some algorithms exclude dangling nodes which are an important part and carry important information of the web graph. In this work, we consider dangling nodes as regular web pages without changing the web graph structure and therefore fully preserve the information carried by them. This differs from some other algorithms which include dangling nodes but treat them differently from regular pages for the purpose of efficiency. We then give an efficient algorithm with negligible overhead associated with dangling node treatment. Moreover, the treatment poses little difficulty in the parallelization of the algorithm.

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


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

rawabstract:
Link analysis is a critical component of current Internet search engines' results ranking software, which determines the ordering of query results returned to the user. The ordering of query results can have an enormous impact on web traffic and the resulting business activity of an enterprise; hence businesses have a strong interest in having their web pages highly ranked in search engine results. This has led to attempts to artificially inflate page ranks by spamming the link structure of the web. Building an artificial condensed link structure called a 'link farm' is one technique to influence a page ranking system, such as the popular PageRank algorithm. In this paper, we present an approach to remove the bias due to link farms from PageRank computation. We propose a method to first measure the PageRank weight accumulated by link farms, and then distribute the weight to other web pages by a modification of the transition matrix in the standard PageRank algorithm. We present results of a selected web graph that is manually spammed. The results show that the proposed approach can effectively reduce the bias from link farms in PageRank computation.

rawbooktitle:
{AINA '07: 21st International Conference on Advanced Information Networking and Applications (2007)}

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


bibtype: inproceedings
filename: pagerank.bib
label: Schatten:2007:OFS
author: M. Schatten and M. Zugaj
title: Organizing a Fishnet Structure
crossref: Luzar-Stiffler:2007:PII
pages: 81--86
year: 2007
DOI: https://doi.org/10.1109/ITI.2007.4283748
bibdate: Thu May 06 15:05:59 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:05:59 ???
acknowledgement: ack-nhfb
rawauthor: M. Schatten and M. Zugaj
rawtitle: Organizing a Fishnet Structure
entry:
@InProceedings{Schatten:2007:OFS,
  author =       "M. Schatten and M. Zugaj",
  title =        "Organizing a Fishnet Structure",
  crossref =     "Luzar-Stiffler:2007:PII",
  pages =        "81--86",
  year =         "2007",
  DOI =          "https://doi.org/10.1109/ITI.2007.4283748",
  bibdate =      "Thu May 06 15:05:59 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
}


bibtype: inproceedings
filename: pagerank.bib
label: Shih:2007:VAR
author: Huang-Chia Shih and Chung-Lin Huang and Jenq-Neng Hwang
crossref: IEEE:2007:IWM
pages: 414--417
year: 2007
DOI: https://doi.org/10.1109/MMSP.2007.4412904
bibdate: Thu May 06 15:01:22 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.06 15:01:22 ???
acknowledgement: ack-nhfb
rawauthor: Huang-Chia Shih and Chung-Lin Huang and Jenq-Neng Hwang
title:
Video Attention Ranking using Visual and Contextual Attention Model for Content-based Sports Videos Mining

rawtitle:
Video Attention Ranking using Visual and Contextual Attention Model for Content-based Sports Videos Mining

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


bibtype: inproceedings
filename: pagerank.bib
label: Volkovich:2007:DFB
author: Yana Volkovich and Nelly Litvak and Debora Donato
title: Determining factors behind the PageRank log-log plot
crossref: Bonato:2007:AMW
pages: 108--123
year: 2007
DOI: https://doi.org/10.1007/978-3-540-77004-6_9
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
MRclass: 68U35 (05C90 68M10 68R10 91D30)
MRnumber: MR2504910
bibdate: Wed May 5 19:28:06 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2010.05.05 19:28:06 ???
series: ser-LNCS
ZMnumber: 1136.68339
acknowledgement: ack-nhfb
rawauthor: Yana Volkovich and Nelly Litvak and Debora Donato
rawtitle: Determining factors behind the {PageRank} log-log plot
entry:
@InProceedings{Volkovich:2007:DFB,
  author =       "Yana Volkovich and Nelly Litvak and Debora Donato",
  title =        "Determining factors behind the {PageRank} log-log
                 plot",
  crossref =     "Bonato:2007:AMW",
  pages =        "108--123",
  year =         "2007",
  DOI =          "https://doi.org/10.1007/978-3-540-77004-6_9",
  ISSN =         "0302-9743 (print), 1611-3349 (electronic)",
  MRclass =      "68U35 (05C90 68M10 68R10 91D30)",
  MRnumber =     "MR2504910",
  bibdate =      "Wed May 5 19:28:06 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  series =       ser-LNCS,
  ZMnumber =     "1136.68339",
  acknowledgement = ack-nhfb,
}


bibtype: article
filename: pagerank.bib
label: Volkovich:2007:SMW
author: Y. Volkovich and D. Donato and N. Litvak
title: Stochastic models for Web ranking
journal: j-SIGMETRICS
volume: 35
number: 3
pages: 53--53
month: dec
monthnumber: 12
year: 2007
DOI: https://doi.org/10.1145/1328690.1328713
ISSN: 0163-5999 (print), 1557-9484 (electronic) OR 01635999 (print), 15579484 (electronic)
ISSNL: 0163-5999 OR 01635999
bibdate: Fri Jun 27 09:42:53 MDT 2008
bibsource: http://portal.acm.org/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2008.06.27 09:42:53 MDT
URL: https://www.math.utah.edu/pub/tex/bib/sigmetrics.bib
fjournal: ACM SIGMETRICS Performance Evaluation Review
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/toc.cfm?id=J618
rawauthor: Y. Volkovich and D. Donato and N. Litvak
rawtitle: Stochastic models for {Web} ranking
abstract:
Web search engines need to deal with hundreds and thousands of pages which are relevant to a user's query. Listing them in the right order is an important and non-trivial task. Thus Google introduced em PageRank [1] as a popularity measure for Web pages. Besides its primary application in search engines, PageRank also became a major method for evaluating importance of nodes in different informational networks and database systems.

rawabstract:
Web search engines need to deal with hundreds and thousands of pages which are relevant to a user's query. Listing them in the right order is an important and non-trivial task. Thus Google introduced {\em PageRank\/} [1] as a popularity measure for Web pages. Besides its primary application in search engines, PageRank also became a major method for evaluating importance of nodes in different informational networks and database systems.

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


