# bibtab

bibtype: article
filename: pagerank.bib
label: Fiala:2017:PBP
author: Dalibor Fiala and Gabriel Tutoky
title: PageRank-based prediction of award-winning researchers and the impact of citations
journal: j-J-INFORMETRICS
volume: 11
number: 4
pages: 1044--1068
month: nov
monthnumber: 11
year: 2017
DOI: https://doi.org/10.1016/j.joi.2017.09.008
ISSN: 1751-1577 (print), 1875-5879 (electronic) OR 17511577 (print), 18755879 (electronic)
ISSNL: 1751-1577 OR 17511577
bibdate: Thu Jul 26 06:36:09 MDT 2018
bibtimestamp: 2018.07.26 06:36:09 MDT
URL: https://www.sciencedirect.com/science/article/pii/S175115771730038X
fjournal: Journal of Informetrics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/17511577/
rawauthor: Dalibor Fiala and Gabriel Tutoky
rawtitle: {PageRank}-based prediction of award-winning researchers and the impact of citations
entry:
@Article{Fiala:2017:PBP,
  author =       "Dalibor Fiala and Gabriel Tutoky",
  title =        "{PageRank}-based prediction of award-winning
                 researchers and the impact of citations",
  journal =      j-J-INFORMETRICS,
  volume =       "11",
  number =       "4",
  pages =        "1044--1068",
  month =        nov,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1016/j.joi.2017.09.008",
  ISSN =         "1751-1577 (print), 1875-5879 (electronic)",
  ISSN-L =       "1751-1577",
  bibdate =      "Thu Jul 26 06:36:09 MDT 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://www.sciencedirect.com/science/article/pii/S175115771730038X",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Informetrics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/17511577/",
}


bibtype: article
filename: pagerank.bib
label: Gu:2017:AIA
author: Chuanqing Gu and Wenwen Wang
title: An Arnoldi--Inout algorithm for computing PageRank problems
journal: j-J-COMPUT-APPL-MATH
volume: 309
pages: 219--229
day: 1
month: jan
monthnumber: 1
year: 2017
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Sat Feb 25 13:35:53 MST 2017
bibtimestamp: 2017.02.25 13:35:53 MST
URL: http://www.sciencedirect.com/science/article/pii/S0377042716302606
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Chuanqing Gu and Wenwen Wang
rawtitle: An {Arnoldi--Inout} algorithm for computing {PageRank} problems
bibsource:
https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Gu:2017:AIA,
  author =       "Chuanqing Gu and Wenwen Wang",
  title =        "An {Arnoldi--Inout} algorithm for computing {PageRank}
                 problems",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "309",
  number =       "??",
  pages =        "219--229",
  day =          "1",
  month =        jan,
  year =         "2017",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Sat Feb 25 13:35:53 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042716302606",
  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: Guo:2017:PPP
author: Wentian Guo and Yuchen Li and Mo Sha and Kian-Lee Tan
title: Parallel personalized pagerank on dynamic graphs
journal: j-PROC-VLDB-ENDOWMENT
volume: 11
number: 1
pages: 93--106
month: sep
monthnumber: 9
year: 2017
ISSN: 2150-8097 OR 21508097
bibdate: Tue Oct 10 17:16:21 MDT 2017
bibtimestamp: 2017.10.10 17:16:21 MDT
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/citation.cfm?id=J1174
rawauthor: Wentian Guo and Yuchen Li and Mo Sha and Kian-Lee Tan
rawtitle: Parallel personalized pagerank on dynamic graphs
abstract:
Personalized PageRank (PPR) is a well-known proximity measure in graphs. To meet the need for dynamic PPR maintenance, recent works have proposed a local update scheme to support incremental computation. Nevertheless, sequential execution of the scheme is still too slow for highspeed stream processing. Therefore, we are motivated to design a parallel approach for dynamic PPR computation. First, as updates always come in batches, we devise a batch processing method to reduce synchronization cost among every single update and enable more parallelism for iterative parallel execution. Our theoretical analysis shows that the parallel approach has the same asymptotic complexity as the sequential approach. Second, we devise novel optimization techniques to effectively reduce runtime overheads for parallel processes. Experimental evaluation shows that our parallel algorithm can achieve orders of magnitude speedups on GPUs and multi-core CPUs compared with the state-of-the-art sequential algorithm.

rawabstract:
Personalized PageRank (PPR) is a well-known proximity measure in graphs. To meet the need for dynamic PPR maintenance, recent works have proposed a local update scheme to support incremental computation. Nevertheless, sequential execution of the scheme is still too slow for highspeed stream processing. Therefore, we are motivated to design a parallel approach for dynamic PPR computation. First, as updates always come in batches, we devise a batch processing method to reduce synchronization cost among every single update and enable more parallelism for iterative parallel execution. Our theoretical analysis shows that the parallel approach has the same asymptotic complexity as the sequential approach. Second, we devise novel optimization techniques to effectively reduce runtime overheads for parallel processes. Experimental evaluation shows that our parallel algorithm can achieve orders of magnitude speedups on GPUs and multi-core CPUs compared with the state-of-the-art sequential algorithm.

entry:
@Article{Guo:2017:PPP,
  author =       "Wentian Guo and Yuchen Li and Mo Sha and Kian-Lee
                 Tan",
  title =        "Parallel personalized pagerank on dynamic graphs",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "11",
  number =       "1",
  pages =        "93--106",
  month =        sep,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "2150-8097",
  bibdate =      "Tue Oct 10 17:16:21 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  abstract =     "Personalized PageRank (PPR) is a well-known proximity
                 measure in graphs. To meet the need for dynamic PPR
                 maintenance, recent works have proposed a local update
                 scheme to support incremental computation.
                 Nevertheless, sequential execution of the scheme is
                 still too slow for highspeed stream processing.
                 Therefore, we are motivated to design a parallel
                 approach for dynamic PPR computation. First, as updates
                 always come in batches, we devise a batch processing
                 method to reduce synchronization cost among every
                 single update and enable more parallelism for iterative
                 parallel execution. Our theoretical analysis shows that
                 the parallel approach has the same asymptotic
                 complexity as the sequential approach. Second, we
                 devise novel optimization techniques to effectively
                 reduce runtime overheads for parallel processes.
                 Experimental evaluation shows that our parallel
                 algorithm can achieve orders of magnitude speedups on
                 GPUs and multi-core CPUs compared with the
                 state-of-the-art sequential algorithm.",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "http://portal.acm.org/citation.cfm?id=J1174",
}


bibtype: article
filename: pagerank.bib
label: Lai:2017:PCL
author: Siyan Lai and Bo Shao and Ying Xu and Xiaola Lin
title: Parallel computations of local PageRank problem based on Graphics Processing Unit
journal: j-CCPE
volume: 29
number: 24
pages: ??--??
day: 25
month: dec
monthnumber: 12
year: 2017
CODEN: CCPEBO
DOI: https://doi.org/10.1002/cpe.4245
ISSN: 1532-0626 (print), 1532-0634 (electronic) OR 15320626 (print), 15320634 (electronic)
ISSNL: 1532-0626 OR 15320626
bibdate: Sat Dec 30 09:11:59 MST 2017
bibsource: https://www.math.utah.edu/pub/tex/bib/ccpe.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2017.12.30 09:11:59 MST
fjournal: Concurrency and Computation: Practice and Experience
acknowledgement: ack-nhfb
journalURL: http://www.interscience.wiley.com/jpages/1532-0626
rawauthor: Siyan Lai and Bo Shao and Ying Xu and Xiaola Lin
rawtitle: Parallel computations of local {PageRank} problem based on {Graphics Processing Unit}
entry:
@Article{Lai:2017:PCL,
  author =       "Siyan Lai and Bo Shao and Ying Xu and Xiaola Lin",
  title =        "Parallel computations of local {PageRank} problem
                 based on {Graphics Processing Unit}",
  journal =      j-CCPE,
  volume =       "29",
  number =       "24",
  pages =        "??--??",
  day =          "25",
  month =        dec,
  year =         "2017",
  CODEN =        "CCPEBO",
  DOI =          "https://doi.org/10.1002/cpe.4245",
  ISSN =         "1532-0626 (print), 1532-0634 (electronic)",
  ISSN-L =       "1532-0626",
  bibdate =      "Sat Dec 30 09:11:59 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ccpe.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Concurrency and Computation: Practice and Experience",
  journal-URL =  "http://www.interscience.wiley.com/jpages/1532-0626",
}


bibtype: article
filename: pagerank.bib
label: Lai:2017:SIP
author: Siyan Lai and Bo Shao and Ying Xu and Xiaola Lin
title: Parallel computations of local PageRank problem based on Graphics Processing Unit
journal: j-CCPE
volume: 29
number: 24
pages: ??--??
day: 25
month: dec
monthnumber: 12
year: 2017
CODEN: CCPEBO
DOI: https://doi.org/10.1002/cpe.4245
ISSN: 1532-0626 (print), 1532-0634 (electronic) OR 15320626 (print), 15320634 (electronic)
ISSNL: 1532-0626 OR 15320626
bibdate: Sat Dec 30 09:11:59 MST 2017
bibsource: https://www.math.utah.edu/pub/tex/bib/ccpe.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2017.12.30 09:11:59 MST
fjournal: Concurrency and Computation: Practice and Experience
acknowledgement: ack-nhfb
journalURL: http://www.interscience.wiley.com/jpages/1532-0626
rawauthor: Siyan Lai and Bo Shao and Ying Xu and Xiaola Lin
rawtitle: Parallel computations of local {PageRank} problem based on {Graphics Processing Unit}
entry:
@Article{Lai:2017:SIP,
  author =       "Siyan Lai and Bo Shao and Ying Xu and Xiaola Lin",
  title =        "Parallel computations of local {PageRank} problem
                 based on {Graphics Processing Unit}",
  journal =      j-CCPE,
  volume =       "29",
  number =       "24",
  pages =        "??--??",
  day =          "25",
  month =        dec,
  year =         "2017",
  CODEN =        "CCPEBO",
  DOI =          "https://doi.org/10.1002/cpe.4245",
  ISSN =         "1532-0626 (print), 1532-0634 (electronic)",
  ISSN-L =       "1532-0626",
  bibdate =      "Sat Dec 30 09:11:59 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ccpe.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Concurrency and Computation: Practice and Experience",
  journal-URL =  "http://www.interscience.wiley.com/jpages/1532-0626",
}


bibtype: article
filename: pagerank.bib
label: Li:2017:UMP
author: Wen Li and Dongdong Liu and Michael K. Ng and Seak-Weng Vong
title: The uniqueness of multilinear PageRank vectors
journal: j-NUM-LIN-ALG-APPL
volume: 24
number: 6
pages: ??--??
month: dec
monthnumber: 12
year: 2017
CODEN: NLAAEM
DOI: https://doi.org/10.1002/nla.2107
ISSN: 1070-5325 (print), 1099-1506 (electronic) OR 10705325 (print), 10991506 (electronic)
bibdate: Sat Dec 30 08:27:16 MST 2017
bibtimestamp: 2017.12.30 08:27:16 MST
fjournal: Numerical Linear Algebra with Applications
acknowledgement: ack-nhfb
journalURL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1506
rawauthor: Wen Li and Dongdong Liu and Michael K. Ng and Seak-Weng Vong
rawtitle: The uniqueness of multilinear {PageRank} vectors
bibsource:
https://www.math.utah.edu/pub/tex/bib/numlinaa.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Li:2017:UMP,
  author =       "Wen Li and Dongdong Liu and Michael K. Ng and
                 Seak-Weng Vong",
  title =        "The uniqueness of multilinear {PageRank} vectors",
  journal =      j-NUM-LIN-ALG-APPL,
  volume =       "24",
  number =       "6",
  pages =        "??--??",
  month =        dec,
  year =         "2017",
  CODEN =        "NLAAEM",
  DOI =          "https://doi.org/10.1002/nla.2107",
  ISSN =         "1070-5325 (print), 1099-1506 (electronic)",
  bibdate =      "Sat Dec 30 08:27:16 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Numerical Linear Algebra with Applications",
  journal-URL =  "http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1506",
}


bibtype: article
filename: pagerank.bib
label: Liu:2017:IPV
author: Qi Liu and Biao Xiang and Nicholas Jing Yuan and Enhong Chen and Hui Xiong and Yi Zheng and Yu Yang
title: An Influence Propagation View of PageRank
journal: j-TKDD
volume: 11
number: 3
pages: 30:1--30:??
month: apr
monthnumber: 4
year: 2017
DOI: https://doi.org/10.1145/3046941
ISSN: 1556-4681 (print), 1556-472X (electronic) OR 15564681 (print), 1556472X (electronic)
ISSNL: 1556-4681 OR 15564681
bibdate: Mon Jul 24 17:32:52 MDT 2017
bibtimestamp: 2017.07.24 17:32:52 MDT
fjournal: ACM Transactions on Knowledge Discovery from Data (TKDD)
acknowledgement: ack-nhfb
articleno: 30
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J1054
rawauthor: Qi Liu and Biao Xiang and Nicholas Jing Yuan and Enhong Chen and Hui Xiong and Yi Zheng and Yu Yang
rawtitle: An Influence Propagation View of {PageRank}
bibsource:
http://www.acm.org/pubs/contents/journals/tkdd/; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tkdd.bib

abstract:
For a long time, PageRank has been widely used for authority computation and has been adopted as a solid baseline for evaluating social influence related applications. However, when measuring the authority of network nodes, the traditional PageRank method does not take the nodes' prior knowledge into consideration. Also, the connection between PageRank and social influence modeling methods is not clearly established. To that end, this article provides a focused study on understanding PageRank as well as the relationship between PageRank and social influence analysis. Along this line, we first propose a linear social influence model and reveal that this model generalizes the PageRank-based authority computation by introducing some constraints. Then, we show that the authority computation by PageRank can be enhanced if exploiting more reasonable constraints (e.g., from prior knowledge). Next, to deal with the computational challenge of linear model with general constraints, we provide an upper bound for identifying nodes with top authorities. Moreover, we extend the proposed linear model for better measuring the authority of the given node sets, and we also demonstrate the way to quickly identify the top authoritative node sets. Finally, extensive experimental evaluations on four real-world networks validate the effectiveness of the proposed linear model with respect to different constraint settings. The results show that the methods with more reasonable constraints can lead to better ranking and recommendation performance. Meanwhile, the upper bounds formed by PageRank values could be used to quickly locate the nodes and node sets with the highest authorities.

rawabstract:
For a long time, PageRank has been widely used for authority computation and has been adopted as a solid baseline for evaluating social influence related applications. However, when measuring the authority of network nodes, the traditional PageRank method does not take the nodes' prior knowledge into consideration. Also, the connection between PageRank and social influence modeling methods is not clearly established. To that end, this article provides a focused study on understanding PageRank as well as the relationship between PageRank and social influence analysis. Along this line, we first propose a linear social influence model and reveal that this model generalizes the PageRank-based authority computation by introducing some constraints. Then, we show that the authority computation by PageRank can be enhanced if exploiting more reasonable constraints (e.g., from prior knowledge). Next, to deal with the computational challenge of linear model with general constraints, we provide an upper bound for identifying nodes with top authorities. Moreover, we extend the proposed linear model for better measuring the authority of the given node sets, and we also demonstrate the way to quickly identify the top authoritative node sets. Finally, extensive experimental evaluations on four real-world networks validate the effectiveness of the proposed linear model with respect to different constraint settings. The results show that the methods with more reasonable constraints can lead to better ranking and recommendation performance. Meanwhile, the upper bounds formed by PageRank values could be used to quickly locate the nodes and node sets with the highest authorities.

entry:
@Article{Liu:2017:IPV,
  author =       "Qi Liu and Biao Xiang and Nicholas Jing Yuan and
                 Enhong Chen and Hui Xiong and Yi Zheng and Yu Yang",
  title =        "An Influence Propagation View of {PageRank}",
  journal =      j-TKDD,
  volume =       "11",
  number =       "3",
  pages =        "30:1--30:??",
  month =        apr,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3046941",
  ISSN =         "1556-4681 (print), 1556-472X (electronic)",
  ISSN-L =       "1556-4681",
  bibdate =      "Mon Jul 24 17:32:52 MDT 2017",
  bibsource =    "http://www.acm.org/pubs/contents/journals/tkdd/;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tkdd.bib",
  abstract =     "For a long time, PageRank has been widely used for
                 authority computation and has been adopted as a solid
                 baseline for evaluating social influence related
                 applications. However, when measuring the authority of
                 network nodes, the traditional PageRank method does not
                 take the nodes' prior knowledge into consideration.
                 Also, the connection between PageRank and social
                 influence modeling methods is not clearly established.
                 To that end, this article provides a focused study on
                 understanding PageRank as well as the relationship
                 between PageRank and social influence analysis. Along
                 this line, we first propose a linear social influence
                 model and reveal that this model generalizes the
                 PageRank-based authority computation by introducing
                 some constraints. Then, we show that the authority
                 computation by PageRank can be enhanced if exploiting
                 more reasonable constraints (e.g., from prior
                 knowledge). Next, to deal with the computational
                 challenge of linear model with general constraints, we
                 provide an upper bound for identifying nodes with top
                 authorities. Moreover, we extend the proposed linear
                 model for better measuring the authority of the given
                 node sets, and we also demonstrate the way to quickly
                 identify the top authoritative node sets. Finally,
                 extensive experimental evaluations on four real-world
                 networks validate the effectiveness of the proposed
                 linear model with respect to different constraint
                 settings. The results show that the methods with more
                 reasonable constraints can lead to better ranking and
                 recommendation performance. Meanwhile, the upper bounds
                 formed by PageRank values could be used to quickly
                 locate the nodes and node sets with the highest
                 authorities.",
  acknowledgement = ack-nhfb,
  articleno =    "30",
  fjournal =     "ACM Transactions on Knowledge Discovery from Data
                 (TKDD)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1054",
}


bibtype: article
filename: pagerank.bib
label: Pradhan:2017:CIP
author: Dinesh Pradhan and Partha Sarathi Paul and Umesh Maheswari and Subrata Nandi and Tanmoy Chakraborty
title: $ C^3 $-index: a PageRank based multi-faceted metric for authors' performance measurement
journal: j-SCIENTOMETRICS
volume: 110
number: 1
pages: 253--273
month: jan
monthnumber: 1
year: 2017
CODEN: SCNTDX
DOI: https://doi.org/10.1007/s11192-016-2168-y
ISSN: 0138-9130 (print), 1588-2861 (electronic) OR 01389130 (print), 15882861 (electronic)
ISSNL: 0138-9130 OR 01389130
bibdate: Mon Jan 30 06:44:49 MST 2017
bibtimestamp: 2017.01.30 06:44:49 MST
URL: http://link.springer.com/accesspage/article/10.1007/s11192-016-2168-y
fjournal: Scientometrics
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11192
rawauthor: Dinesh Pradhan and Partha Sarathi Paul and Umesh Maheswari and Subrata Nandi and Tanmoy Chakraborty
rawtitle: {$ C^3 $}-index: a {PageRank} based multi-faceted metric for authors' performance measurement
bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib

entry:
@Article{Pradhan:2017:CIP,
  author =       "Dinesh Pradhan and Partha Sarathi Paul and Umesh
                 Maheswari and Subrata Nandi and Tanmoy Chakraborty",
  title =        "{$ C^3 $}-index: a {PageRank} based multi-faceted
                 metric for authors' performance measurement",
  journal =      j-SCIENTOMETRICS,
  volume =       "110",
  number =       "1",
  pages =        "253--273",
  month =        jan,
  year =         "2017",
  CODEN =        "SCNTDX",
  DOI =          "https://doi.org/10.1007/s11192-016-2168-y",
  ISSN =         "0138-9130 (print), 1588-2861 (electronic)",
  ISSN-L =       "0138-9130",
  bibdate =      "Mon Jan 30 06:44:49 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib",
  URL =          "http://link.springer.com/accesspage/article/10.1007/s11192-016-2168-y",
  acknowledgement = ack-nhfb,
  fjournal =     "Scientometrics",
  journal-URL =  "http://link.springer.com/journal/11192",
}


bibtype: article
filename: pagerank.bib
label: Rafailidis:2017:LSS
author: D. Rafailidis and E. Constantinou and Y. Manolopoulos
title: Landmark selection for spectral clustering based on Weighted PageRank
journal: j-FUT-GEN-COMP-SYS
volume: 68
pages: 465--472
month: mar
monthnumber: 3
year: 2017
CODEN: FGSEVI
ISSN: 0167-739X (print), 1872-7115 (electronic) OR 0167739X (print), 18727115 (electronic)
ISSNL: 0167-739X OR 0167739X
bibdate: Sat Dec 10 08:32:13 MST 2016
bibtimestamp: 2016.12.10 08:32:13 MST
URL: http://www.sciencedirect.com/science/article/pii/S0167739X16300504
fjournal: Future Generation Computer Systems
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/0167739X/
rawauthor: D. Rafailidis and E. Constantinou and Y. Manolopoulos
rawtitle: Landmark selection for spectral clustering based on {Weighted PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/futgencompsys.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Rafailidis:2017:LSS,
  author =       "D. Rafailidis and E. Constantinou and Y.
                 Manolopoulos",
  title =        "Landmark selection for spectral clustering based on
                 {Weighted PageRank}",
  journal =      j-FUT-GEN-COMP-SYS,
  volume =       "68",
  number =       "??",
  pages =        "465--472",
  month =        mar,
  year =         "2017",
  CODEN =        "FGSEVI",
  ISSN =         "0167-739X (print), 1872-7115 (electronic)",
  ISSN-L =       "0167-739X",
  bibdate =      "Sat Dec 10 08:32:13 MST 2016",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/futgencompsys.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0167739X16300504",
  acknowledgement = ack-nhfb,
  fjournal =     "Future Generation Computer Systems",
  journal-URL =  "http://www.sciencedirect.com/science/journal/0167739X/",
}


bibtype: article
filename: pagerank.bib
label: Reinstaller:2017:UPA
author: Andreas Reinstaller and Peter Reschenhofer
journal: j-SCIENTOMETRICS
volume: 113
number: 3
pages: 1407--1438
month: dec
monthnumber: 12
year: 2017
CODEN: SCNTDX
DOI: https://doi.org/10.1007/s11192-017-2549-x
ISSN: 0138-9130 (print), 1588-2861 (electronic) OR 01389130 (print), 15882861 (electronic)
ISSNL: 0138-9130 OR 01389130
bibdate: Tue Nov 21 07:25:48 MST 2017
bibtimestamp: 2017.11.21 07:25:48 MST
URL: http://link.springer.com/article/10.1007/s11192-017-2549-x
fjournal: Scientometrics
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11192
rawauthor: Andreas Reinstaller and Peter Reschenhofer
title:
Using PageRank in the analysis of technological progress through patents: an illustration for biotechnological inventions

rawtitle:
Using {PageRank} in the analysis of technological progress through patents: an illustration for biotechnological inventions

entry:
@Article{Reinstaller:2017:UPA,
  author =       "Andreas Reinstaller and Peter Reschenhofer",
  title =        "Using {PageRank} in the analysis of technological
                 progress through patents: an illustration for
                 biotechnological inventions",
  journal =      j-SCIENTOMETRICS,
  volume =       "113",
  number =       "3",
  pages =        "1407--1438",
  month =        dec,
  year =         "2017",
  CODEN =        "SCNTDX",
  DOI =          "https://doi.org/10.1007/s11192-017-2549-x",
  ISSN =         "0138-9130 (print), 1588-2861 (electronic)",
  ISSN-L =       "0138-9130",
  bibdate =      "Tue Nov 21 07:25:48 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib",
  URL =          "http://link.springer.com/article/10.1007/s11192-017-2549-x",
  acknowledgement = ack-nhfb,
  fjournal =     "Scientometrics",
  journal-URL =  "http://link.springer.com/journal/11192",
}


bibtype: article
filename: pagerank.bib
label: Shao:2017:DSA
author: Fei Shao and Rong Peng and Han Lai and Bangchao Wang
title: DRank: a semi-automated requirements prioritization method based on preferences and dependencies
journal: j-J-SYST-SOFTW
volume: 126
pages: 141--156
month: apr
monthnumber: 4
year: 2017
CODEN: JSSODM
DOI: https://doi.org/10.1016/j.jss.2016.09.043
ISSN: 0164-1212 (print), 1873-1228 (electronic) OR 01641212 (print), 18731228 (electronic)
ISSNL: 0164-1212 OR 01641212
bibdate: Fri Feb 10 10:22:09 MST 2017
bibtimestamp: 2017.02.10 10:22:09 MST
URL: http://www.sciencedirect.com/science/article/pii/S0164121216301911
fjournal: Journal of Systems and Software
keywords: DRank; PageRank-Req; Prioritization Evaluation Attributes Tree (PEAT)
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/01641212/
rawauthor: Fei Shao and Rong Peng and Han Lai and Bangchao Wang
rawtitle: {DRank}: a semi-automated requirements prioritization method based on preferences and dependencies
bibsource:
https://www.math.utah.edu/pub/tex/bib/jsystsoftw.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Shao:2017:DSA,
  author =       "Fei Shao and Rong Peng and Han Lai and Bangchao Wang",
  title =        "{DRank}: a semi-automated requirements prioritization
                 method based on preferences and dependencies",
  journal =      j-J-SYST-SOFTW,
  volume =       "126",
  number =       "??",
  pages =        "141--156",
  month =        apr,
  year =         "2017",
  CODEN =        "JSSODM",
  DOI =          "https://doi.org/10.1016/j.jss.2016.09.043",
  ISSN =         "0164-1212 (print), 1873-1228 (electronic)",
  ISSN-L =       "0164-1212",
  bibdate =      "Fri Feb 10 10:22:09 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jsystsoftw.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0164121216301911",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Systems and Software",
  journal-URL =  "http://www.sciencedirect.com/science/journal/01641212/",
  keywords =     "DRank; PageRank-Req; Prioritization Evaluation
                 Attributes Tree (PEAT)",
}


bibtype: article
filename: pagerank.bib
label: Shen:2017:EES
author: Zhao-Li Shen and Ting-Zhu Huang and Bruno Carpentieri and Xian-Ming Gu and Chun Wen
title: An efficient elimination strategy for solving PageRank problems
journal: j-APPL-MATH-COMP
volume: 298
pages: 111--122
day: 1
month: apr
monthnumber: 4
year: 2017
CODEN: AMHCBQ
DOI: https://doi.org/10.1016/j.amc.2016.10.031
ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
bibdate: Fri Dec 23 12:38:50 MST 2016
bibtimestamp: 2016.12.23 12:38:50 MST
URL: http://www.sciencedirect.com/science/article/pii/S0096300316306385
fjournal: Applied Mathematics and Computation
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00963003/
rawauthor: Zhao-Li Shen and Ting-Zhu Huang and Bruno Carpentieri and Xian-Ming Gu and Chun Wen
rawtitle: An efficient elimination strategy for solving {PageRank} problems
bibsource:
https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Shen:2017:EES,
  author =       "Zhao-Li Shen and Ting-Zhu Huang and Bruno Carpentieri
                 and Xian-Ming Gu and Chun Wen",
  title =        "An efficient elimination strategy for solving
                 {PageRank} problems",
  journal =      j-APPL-MATH-COMP,
  volume =       "298",
  number =       "??",
  pages =        "111--122",
  day =          "1",
  month =        apr,
  year =         "2017",
  CODEN =        "AMHCBQ",
  DOI =          "https://doi.org/10.1016/j.amc.2016.10.031",
  ISSN =         "0096-3003 (print), 1873-5649 (electronic)",
  ISSN-L =       "0096-3003",
  bibdate =      "Fri Dec 23 12:38:50 MST 2016",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0096300316306385",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Mathematics and Computation",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00963003/",
}


bibtype: article
filename: pagerank.bib
label: Tan:2017:NEM
author: Xueyuan Tan
title: A new extrapolation method for PageRank computations
journal: j-J-COMPUT-APPL-MATH
volume: 313
pages: 383--392
day: 15
month: mar
monthnumber: 3
year: 2017
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Sat Feb 25 13:36:49 MST 2017
bibtimestamp: 2017.02.25 13:36:49 MST
URL: http://www.sciencedirect.com/science/article/pii/S0377042716304034
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Xueyuan Tan
rawtitle: A new extrapolation method for {PageRank} computations
entry:
@Article{Tan:2017:NEM,
  author =       "Xueyuan Tan",
  title =        "A new extrapolation method for {PageRank}
                 computations",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "313",
  number =       "??",
  pages =        "383--392",
  day =          "15",
  month =        mar,
  year =         "2017",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Sat Feb 25 13:36:49 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042716304034",
  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: Wen:2017:NTS
author: Chun Wen and Ting-Zhu Huang and Zhao-Li Shen
title: A note on the two-step matrix splitting iteration for computing PageRank
journal: j-J-COMPUT-APPL-MATH
volume: 315
pages: 87--97
day: 1
month: may
monthnumber: 5
year: 2017
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Sat Feb 25 13:36:50 MST 2017
bibtimestamp: 2017.02.25 13:36:50 MST
URL: http://www.sciencedirect.com/science/article/pii/S037704271630509X
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Chun Wen and Ting-Zhu Huang and Zhao-Li Shen
rawtitle: A note on the two-step matrix splitting iteration for computing {PageRank}
entry:
@Article{Wen:2017:NTS,
  author =       "Chun Wen and Ting-Zhu Huang and Zhao-Li Shen",
  title =        "A note on the two-step matrix splitting iteration for
                 computing {PageRank}",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "315",
  number =       "??",
  pages =        "87--97",
  day =          "1",
  month =        may,
  year =         "2017",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Sat Feb 25 13:36:50 MST 2017",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S037704271630509X",
  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: Avrachenkov:2018:MFA
author: Konstantin Avrachenkov and Arun Kadavankandy and Nelly Litvak
title: Mean Field Analysis of Personalized PageRank with Implications for Local Graph Clustering
journal: j-J-STAT-PHYS
volume: 173
number: 3--4
pages: 895--916
month: nov
monthnumber: 11
year: 2018
CODEN: JSTPSB
DOI: https://doi.org/10.1007/s10955-018-2099-5
ISSN: 0022-4715 (print), 1572-9613 (electronic) OR 00224715 (print), 15729613 (electronic)
ISSNL: 0022-4715 OR 00224715
bibdate: Fri Mar 1 07:23:16 MST 2019
bibtimestamp: 2019.03.01 07:23:16 MST
fjournal: Journal of Statistical Physics
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/10955
rawauthor: Konstantin Avrachenkov and Arun Kadavankandy and Nelly Litvak
rawtitle: Mean Field Analysis of Personalized {PageRank} with Implications for Local Graph Clustering
bibsource:
https://www.math.utah.edu/pub/tex/bib/jstatphys2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Avrachenkov:2018:MFA,
  author =       "Konstantin Avrachenkov and Arun Kadavankandy and Nelly
                 Litvak",
  title =        "Mean Field Analysis of Personalized {PageRank} with
                 Implications for Local Graph Clustering",
  journal =      j-J-STAT-PHYS,
  volume =       "173",
  number =       "3--4",
  pages =        "895--916",
  month =        nov,
  year =         "2018",
  CODEN =        "JSTPSB",
  DOI =          "https://doi.org/10.1007/s10955-018-2099-5",
  ISSN =         "0022-4715 (print), 1572-9613 (electronic)",
  ISSN-L =       "0022-4715",
  bibdate =      "Fri Mar 1 07:23:16 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jstatphys2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Statistical Physics",
  journal-URL =  "http://link.springer.com/journal/10955",
}


bibtype: article
filename: pagerank.bib
label: Boldi:2018:BMC
author: Paolo Boldi and Andrea Marino and Massimo Santini and Sebastiano Vigna
title: BUbiNG: Massive Crawling for the Masses
journal: j-TWEB
volume: 12
number: 2
pages: 12:1--12:26
month: jun
monthnumber: 6
year: 2018
DOI: https://doi.org/10.1145/3160017
ISSN: 1559-1131 (print), 1559-114X (electronic) OR 15591131 (print), 1559114X (electronic)
ISSNL: 1559-1131 OR 15591131
bibdate: Thu Jun 28 14:10:01 MDT 2018
bibtimestamp: 2018.06.28 14:10:01 MDT
URL: https://dl.acm.org/citation.cfm?doid=3176641.3160017
fjournal: ACM Transactions on the Web (TWEB)
keywords: BUbiNG; centrality measures; distributed systems; Java; PageRank; UbiCrawler; Web crawling
acknowledgement: ack-nhfb
articleno: 12
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J1062
rawauthor: Paolo Boldi and Andrea Marino and Massimo Santini and Sebastiano Vigna
rawtitle: {BUbiNG}: Massive Crawling for the Masses
bibsource:
https://www.math.utah.edu/pub/tex/bib/java2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tweb.bib

abstract:
Although web crawlers have been around for twenty years by now, there is virtually no freely available, open-source crawling software that guarantees high throughput, overcomes the limits of single-machine systems, and, at the same time, scales linearly with the amount of resources available. This article aims at filling this gap, through the description of BUbiNG, our next-generation web crawler built upon the authors' experience with UbiCrawler [9] and on the last ten years of research on the topic. BUbiNG is an open-source Java fully distributed crawler; a single BUbiNG agent, using sizeable hardware, can crawl several thousand pages per second respecting strict politeness constraints, both host- and IP-based. Unlike existing open-source distributed crawlers that rely on batch techniques (like MapReduce), BUbiNG job distribution is based on modern high-speed protocols to achieve very high throughput.

rawabstract:
Although web crawlers have been around for twenty years by now, there is virtually no freely available, open-source crawling software that guarantees high throughput, overcomes the limits of single-machine systems, and, at the same time, scales linearly with the amount of resources available. This article aims at filling this gap, through the description of BUbiNG, our next-generation web crawler built upon the authors' experience with UbiCrawler [9] and on the last ten years of research on the topic. BUbiNG is an open-source Java fully distributed crawler; a single BUbiNG agent, using sizeable hardware, can crawl several thousand pages per second respecting strict politeness constraints, both host- and IP-based. Unlike existing open-source distributed crawlers that rely on batch techniques (like MapReduce), BUbiNG job distribution is based on modern high-speed protocols to achieve very high throughput.

entry:
@Article{Boldi:2018:BMC,
  author =       "Paolo Boldi and Andrea Marino and Massimo Santini and
                 Sebastiano Vigna",
  title =        "{BUbiNG}: Massive Crawling for the Masses",
  journal =      j-TWEB,
  volume =       "12",
  number =       "2",
  pages =        "12:1--12:26",
  month =        jun,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3160017",
  ISSN =         "1559-1131 (print), 1559-114X (electronic)",
  ISSN-L =       "1559-1131",
  bibdate =      "Thu Jun 28 14:10:01 MDT 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/java2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tweb.bib",
  URL =          "https://dl.acm.org/citation.cfm?doid=3176641.3160017",
  abstract =     "Although web crawlers have been around for twenty
                 years by now, there is virtually no freely available,
                 open-source crawling software that guarantees high
                 throughput, overcomes the limits of single-machine
                 systems, and, at the same time, scales linearly with
                 the amount of resources available. This article aims at
                 filling this gap, through the description of BUbiNG,
                 our next-generation web crawler built upon the authors'
                 experience with UbiCrawler [9] and on the last ten
                 years of research on the topic. BUbiNG is an
                 open-source Java fully distributed crawler; a single
                 BUbiNG agent, using sizeable hardware, can crawl
                 several thousand pages per second respecting strict
                 politeness constraints, both host- and IP-based. Unlike
                 existing open-source distributed crawlers that rely on
                 batch techniques (like MapReduce), BUbiNG job
                 distribution is based on modern high-speed protocols to
                 achieve very high throughput.",
  acknowledgement = ack-nhfb,
  articleno =    "12",
  fjournal =     "ACM Transactions on the Web (TWEB)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1062",
  keywords =     "BUbiNG; centrality measures; distributed systems;
                 Java; PageRank; UbiCrawler; Web crawling",
}


bibtype: article
filename: pagerank.bib
label: Cui:2018:UDR
author: Yi Cui and Clint Sparkman and Hsin-Tsang Lee and Dmitri Loguinov
title: Unsupervised Domain Ranking in Large-Scale Web Crawls
journal: j-TWEB
volume: 12
number: 4
pages: 26:1--26:??
month: nov
monthnumber: 11
year: 2018
DOI: https://doi.org/10.1145/3182180
ISSN: 1559-1131 (print), 1559-114X (electronic) OR 15591131 (print), 1559114X (electronic)
ISSNL: 1559-1131 OR 15591131
bibdate: Tue Oct 22 08:10:06 MDT 2019
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tweb.bib
bibtimestamp: 2019.10.22 08:10:06 MDT
fjournal: ACM Transactions on the Web (TWEB)
acknowledgement: ack-nhfb
articleno: 26
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J1062
rawauthor: Yi Cui and Clint Sparkman and Hsin-Tsang Lee and Dmitri Loguinov
rawtitle: Unsupervised Domain Ranking in Large-Scale {Web} Crawls
abstract:
With the proliferation of web spam and infinite autogenerated web content, large-scale web crawlers require low-complexity ranking methods to effectively budget their limited resources and allocate bandwidth to reputable sites. In this work, we assume crawls that produce frontiers orders of magnitude larger than RAM, where sorting of pending URLs is infeasible in real time. Under these constraints, the main objective is to quickly compute domain budgets and decide which of them can be massively crawled. Those ranked at the top of the list receive aggressive crawling allowances, while all other domains are visited at some small default rate. To shed light on Internet-wide spam avoidance, we study topology-based ranking algorithms on domain-level graphs from the two largest academic crawls: a 6.3B-page IRLbot dataset and a 1B-page ClueWeb09 exploration. We first propose a new methodology for comparing the various rankings and then show that in-degree BFS-based techniques decisively outperform classic PageRank-style methods, including TrustRank. However, since BFS requires several orders of magnitude higher overhead and is generally infeasible for real-time use, we propose a fast, accurate, and scalable estimation method called TSE that can achieve much better crawl prioritization in practice. It is especially beneficial in applications with limited hardware resources.

rawabstract:
With the proliferation of web spam and infinite autogenerated web content, large-scale web crawlers require low-complexity ranking methods to effectively budget their limited resources and allocate bandwidth to reputable sites. In this work, we assume crawls that produce frontiers orders of magnitude larger than RAM, where sorting of pending URLs is infeasible in real time. Under these constraints, the main objective is to quickly compute domain budgets and decide which of them can be massively crawled. Those ranked at the top of the list receive aggressive crawling allowances, while all other domains are visited at some small default rate. To shed light on Internet-wide spam avoidance, we study topology-based ranking algorithms on domain-level graphs from the two largest academic crawls: a 6.3B-page IRLbot dataset and a 1B-page ClueWeb09 exploration. We first propose a new methodology for comparing the various rankings and then show that in-degree BFS-based techniques decisively outperform classic PageRank-style methods, including TrustRank. However, since BFS requires several orders of magnitude higher overhead and is generally infeasible for real-time use, we propose a fast, accurate, and scalable estimation method called TSE that can achieve much better crawl prioritization in practice. It is especially beneficial in applications with limited hardware resources.

entry:
@Article{Cui:2018:UDR,
  author =       "Yi Cui and Clint Sparkman and Hsin-Tsang Lee and
                 Dmitri Loguinov",
  title =        "Unsupervised Domain Ranking in Large-Scale {Web}
                 Crawls",
  journal =      j-TWEB,
  volume =       "12",
  number =       "4",
  pages =        "26:1--26:??",
  month =        nov,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3182180",
  ISSN =         "1559-1131 (print), 1559-114X (electronic)",
  ISSN-L =       "1559-1131",
  bibdate =      "Tue Oct 22 08:10:06 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tweb.bib",
  abstract =     "With the proliferation of web spam and infinite
                 autogenerated web content, large-scale web crawlers
                 require low-complexity ranking methods to effectively
                 budget their limited resources and allocate bandwidth
                 to reputable sites. In this work, we assume crawls that
                 produce frontiers orders of magnitude larger than RAM,
                 where sorting of pending URLs is infeasible in real
                 time. Under these constraints, the main objective is to
                 quickly compute domain budgets and decide which of them
                 can be massively crawled. Those ranked at the top of
                 the list receive aggressive crawling allowances, while
                 all other domains are visited at some small default
                 rate. To shed light on Internet-wide spam avoidance, we
                 study topology-based ranking algorithms on domain-level
                 graphs from the two largest academic crawls: a
                 6.3B-page IRLbot dataset and a 1B-page ClueWeb09
                 exploration. We first propose a new methodology for
                 comparing the various rankings and then show that
                 in-degree BFS-based techniques decisively outperform
                 classic PageRank-style methods, including TrustRank.
                 However, since BFS requires several orders of magnitude
                 higher overhead and is generally infeasible for
                 real-time use, we propose a fast, accurate, and
                 scalable estimation method called TSE that can achieve
                 much better crawl prioritization in practice. It is
                 especially beneficial in applications with limited
                 hardware resources.",
  acknowledgement = ack-nhfb,
  articleno =    "26",
  fjournal =     "ACM Transactions on the Web (TWEB)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1062",
}


bibtype: article
filename: pagerank.bib
label: Gu:2018:GPA
author: Chuanqing Gu and Xianglong Jiang and Chenchen Shao and Zhibing Chen
title: A GMRES-Power algorithm for computing PageRank problems
journal: j-J-COMPUT-APPL-MATH
volume: 343
pages: 113--123
day: 1
month: dec
monthnumber: 12
year: 2018
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2018.03.017
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Fri Aug 10 18:10:42 MDT 2018
bibtimestamp: 2018.08.10 18:10:42 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0377042718301638
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Chuanqing Gu and Xianglong Jiang and Chenchen Shao and Zhibing Chen
rawtitle: A {GMRES-Power} algorithm for computing {PageRank} problems
entry:
@Article{Gu:2018:GPA,
  author =       "Chuanqing Gu and Xianglong Jiang and Chenchen Shao and
                 Zhibing Chen",
  title =        "A {GMRES-Power} algorithm for computing {PageRank}
                 problems",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "343",
  number =       "??",
  pages =        "113--123",
  day =          "1",
  month =        dec,
  year =         "2018",
  CODEN =        "JCAMDI",
  DOI =          "https://doi.org/10.1016/j.cam.2018.03.017",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Fri Aug 10 18:10:42 MDT 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042718301638",
  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: Gu:2018:PMS
author: Chuanqing Gu and Xianglong Jiang and Ying Nie and Zhibing Chen
title: A preprocessed multi-step splitting iteration for computing PageRank
journal: j-APPL-MATH-COMP
volume: 338
pages: 72--86
day: 1
month: dec
monthnumber: 12
year: 2018
CODEN: AMHCBQ
DOI: https://doi.org/10.1016/j.amc.2018.05.033
ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
bibdate: Fri Sep 14 08:14:14 MDT 2018
bibtimestamp: 2018.09.14 08:14:14 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0096300318304429
fjournal: Applied Mathematics and Computation
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00963003
rawauthor: Chuanqing Gu and Xianglong Jiang and Ying Nie and Zhibing Chen
rawtitle: A preprocessed multi-step splitting iteration for computing {PageRank}
entry:
@Article{Gu:2018:PMS,
  author =       "Chuanqing Gu and Xianglong Jiang and Ying Nie and
                 Zhibing Chen",
  title =        "A preprocessed multi-step splitting iteration for
                 computing {PageRank}",
  journal =      j-APPL-MATH-COMP,
  volume =       "338",
  number =       "??",
  pages =        "72--86",
  day =          "1",
  month =        dec,
  year =         "2018",
  CODEN =        "AMHCBQ",
  DOI =          "https://doi.org/10.1016/j.amc.2018.05.033",
  ISSN =         "0096-3003 (print), 1873-5649 (electronic)",
  ISSN-L =       "0096-3003",
  bibdate =      "Fri Sep 14 08:14:14 MDT 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0096300318304429",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Mathematics and Computation",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00963003",
}


bibtype: article
filename: pagerank.bib
label: Ikegami:2018:PTM
author: Kenshin Ikegami and Yukio Ohsawa
journal: j-FUND-INFO
volume: 159
number: 3
pages: 257--277
year: 2018
CODEN: FUMAAJ
DOI: https://doi.org/10.3233/FI-2018-1664
ISSN: 0169-2968 (print), 1875-8681 (electronic) OR 01692968 (print), 18758681 (electronic)
ISSNL: 0169-2968 OR 01692968
bibdate: Fri Sep 21 07:16:40 MDT 2018
bibtimestamp: 2018.09.21 07:16:40 MDT
fjournal: Fundamenta Informaticae
acknowledgement: ack-nhfb
journalURL: http://content.iospress.com/journals/fundamenta-informaticae
rawauthor: Kenshin Ikegami and Yukio Ohsawa
title:
PageRank Topic Model: Estimation of Multinomial Distributions using Network Structure Analysis Methods

bibsource:
https://www.math.utah.edu/pub/tex/bib/fundinfo2010.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

rawtitle:
{PageRank} Topic Model: Estimation of Multinomial Distributions using Network Structure Analysis Methods

entry:
@Article{Ikegami:2018:PTM,
  author =       "Kenshin Ikegami and Yukio Ohsawa",
  title =        "{PageRank} Topic Model: Estimation of Multinomial
                 Distributions using Network Structure Analysis
                 Methods",
  journal =      j-FUND-INFO,
  volume =       "159",
  number =       "3",
  pages =        "257--277",
  month =        "????",
  year =         "2018",
  CODEN =        "FUMAAJ",
  DOI =          "https://doi.org/10.3233/FI-2018-1664",
  ISSN =         "0169-2968 (print), 1875-8681 (electronic)",
  ISSN-L =       "0169-2968",
  bibdate =      "Fri Sep 21 07:16:40 MDT 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/fundinfo2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Fundamenta Informaticae",
  journal-URL =  "http://content.iospress.com/journals/fundamenta-informaticae",
}


bibtype: article
filename: pagerank.bib
label: Meini:2018:PBA
author: Beatrice Meini and Federico Poloni
title: Perron-based algorithms for the multilinear PageRank
journal: j-NUM-LIN-ALG-APPL
volume: 25
number: 6
pages: ??--??
month: dec
monthnumber: 12
year: 2018
CODEN: NLAAEM
DOI: https://doi.org/10.1002/nla.2177
ISSN: 1070-5325 (print), 1099-1506 (electronic) OR 10705325 (print), 10991506 (electronic)
ISSNL: 1070-5325 OR 10705325
bibdate: Tue Jan 29 12:09:28 MST 2019
bibtimestamp: 2019.01.29 12:09:28 MST
fjournal: Numerical Linear Algebra with Applications
acknowledgement: ack-nhfb
articleno: e2177
journalURL: http://www3.interscience.wiley.com/cgi-bin/jhome/5957
onlinedate: 16 April 2018
rawauthor: Beatrice Meini and Federico Poloni
rawtitle: {Perron}-based algorithms for the multilinear {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/numlinaa.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Meini:2018:PBA,
  author =       "Beatrice Meini and Federico Poloni",
  title =        "{Perron}-based algorithms for the multilinear
                 {PageRank}",
  journal =      j-NUM-LIN-ALG-APPL,
  volume =       "25",
  number =       "6",
  pages =        "??--??",
  month =        dec,
  year =         "2018",
  CODEN =        "NLAAEM",
  DOI =          "https://doi.org/10.1002/nla.2177",
  ISSN =         "1070-5325 (print), 1099-1506 (electronic)",
  ISSN-L =       "1070-5325",
  bibdate =      "Tue Jan 29 12:09:28 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  articleno =    "e2177",
  fjournal =     "Numerical Linear Algebra with Applications",
  journal-URL =  "http://www3.interscience.wiley.com/cgi-bin/jhome/5957",
  onlinedate =   "16 April 2018",
}


bibtype: article
filename: pagerank.bib
label: Mendes:2018:PCM
author: I. R. Mendes and P. B. Vasconcelos
title: PageRank Computation with MAAOR and Lumping Methods
journal: j-MATH-COMPUT-SCI
volume: 12
number: 2
pages: 129--141
month: jun
monthnumber: 6
year: 2018
DOI: https://doi.org/10.1007/s11786-018-0335-7
ISSN: 1661-8270 (print), 1661-8289 (electronic) OR 16618270 (print), 16618289 (electronic)
ISSNL: 1661-8270 OR 16618270
bibdate: Mon Mar 4 06:59:44 MST 2019
bibtimestamp: 2019.03.04 06:59:44 MST
fjournal: Mathematics in Computer Science
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11786
rawauthor: I. R. Mendes and P. B. Vasconcelos
rawtitle: {PageRank} Computation with {MAAOR} and Lumping Methods
bibsource:
http://link.springer.com/journal/11786/12/2; https://www.math.utah.edu/pub/tex/bib/math-comput-sci.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Mendes:2018:PCM,
  author =       "I. R. Mendes and P. B. Vasconcelos",
  title =        "{PageRank} Computation with {MAAOR} and Lumping
                 Methods",
  journal =      j-MATH-COMPUT-SCI,
  volume =       "12",
  number =       "2",
  pages =        "129--141",
  month =        jun,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1007/s11786-018-0335-7",
  ISSN =         "1661-8270 (print), 1661-8289 (electronic)",
  ISSN-L =       "1661-8270",
  bibdate =      "Mon Mar 4 06:59:44 MST 2019",
  bibsource =    "http://link.springer.com/journal/11786/12/2;
                 https://www.math.utah.edu/pub/tex/bib/math-comput-sci.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Mathematics in Computer Science",
  journal-URL =  "http://link.springer.com/journal/11786",
}


bibtype: article
filename: pagerank.bib
label: Miyata:2018:HSA
author: Takafumi Miyata
title: A heuristic search algorithm based on subspaces for PageRank computation
journal: j-J-SUPERCOMPUTING
volume: 74
number: 7
pages: 3278--3294
month: jul
monthnumber: 7
year: 2018
CODEN: JOSUED
DOI: https://doi.org/10.1007/s11227-018-2383-9
ISSN: 0920-8542 (print), 1573-0484 (electronic) OR 09208542 (print), 15730484 (electronic)
ISSNL: 0920-8542 OR 09208542
bibdate: Thu Oct 10 15:31:13 MDT 2019
bibtimestamp: 2019.10.10 15:31:13 MDT
fjournal: The Journal of Supercomputing
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11227
rawauthor: Takafumi Miyata
rawtitle: A heuristic search algorithm based on subspaces for {PageRank} computation
bibsource:
http://link.springer.com/journal/11227/74/7; https://www.math.utah.edu/pub/tex/bib/jsuper.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Miyata:2018:HSA,
  author =       "Takafumi Miyata",
  title =        "A heuristic search algorithm based on subspaces for
                 {PageRank} computation",
  journal =      j-J-SUPERCOMPUTING,
  volume =       "74",
  number =       "7",
  pages =        "3278--3294",
  month =        jul,
  year =         "2018",
  CODEN =        "JOSUED",
  DOI =          "https://doi.org/10.1007/s11227-018-2383-9",
  ISSN =         "0920-8542 (print), 1573-0484 (electronic)",
  ISSN-L =       "0920-8542",
  bibdate =      "Thu Oct 10 15:31:13 MDT 2019",
  bibsource =    "http://link.springer.com/journal/11227/74/7;
                 https://www.math.utah.edu/pub/tex/bib/jsuper.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "The Journal of Supercomputing",
  journal-URL =  "http://link.springer.com/journal/11227",
}


bibtype: article
filename: pagerank.bib
label: Pedroche:2018:SEP
author: Francisco Pedroche and Esther Garcia and Miguel Romance and Regino Criado
title: Sharp estimates for the personalized Multiplex PageRank
journal: j-J-COMPUT-APPL-MATH
volume: 330
pages: 1030--1040
day: 1
month: mar
monthnumber: 3
year: 2018
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Fri Jan 12 08:18:04 MST 2018
bibtimestamp: 2018.01.12 08:18:04 MST
URL: http://www.sciencedirect.com/science/article/pii/S0377042717300717
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Francisco Pedroche and Esther Garc{\'\i}a and Miguel Romance and Regino Criado
rawtitle: Sharp estimates for the personalized Multiplex {PageRank}
entry:
@Article{Pedroche:2018:SEP,
  author =       "Francisco Pedroche and Esther Garc{\'\i}a and Miguel
                 Romance and Regino Criado",
  title =        "Sharp estimates for the personalized Multiplex
                 {PageRank}",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "330",
  number =       "??",
  pages =        "1030--1040",
  day =          "1",
  month =        mar,
  year =         "2018",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Fri Jan 12 08:18:04 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042717300717",
  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: Pedroche:2018:STL
author: Francisco Pedroche and Esther Garcia and Miguel Romance and Regino Criado
title: On the spectrum of two-layer approach and Multiplex PageRank
journal: j-J-COMPUT-APPL-MATH
volume: 344
pages: 161--172
day: 15
month: dec
monthnumber: 12
year: 2018
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2018.05.033
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Fri Aug 10 18:10:43 MDT 2018
bibtimestamp: 2018.08.10 18:10:43 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0377042718303042
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Francisco Pedroche and Esther Garc{\'\i}a and Miguel Romance and Regino Criado
rawtitle: On the spectrum of two-layer approach and {Multiplex PageRank}
entry:
@Article{Pedroche:2018:STL,
  author =       "Francisco Pedroche and Esther Garc{\'\i}a and Miguel
                 Romance and Regino Criado",
  title =        "On the spectrum of two-layer approach and {Multiplex
                 PageRank}",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "344",
  number =       "??",
  pages =        "161--172",
  day =          "15",
  month =        dec,
  year =         "2018",
  CODEN =        "JCAMDI",
  DOI =          "https://doi.org/10.1016/j.cam.2018.05.033",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Fri Aug 10 18:10:43 MDT 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042718303042",
  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: Zhang:2018:CTP
author: Yongjun Zhang and Jialin Ma and Zijian Wang and Bolun Chen and Yongtao Yu
journal: j-SCIENTOMETRICS
volume: 114
number: 3
pages: 1345--1372
month: mar
monthnumber: 3
year: 2018
CODEN: SCNTDX
DOI: https://doi.org/10.1007/s11192-017-2626-1
ISSN: 0138-9130 (print), 1588-2861 (electronic) OR 01389130 (print), 15882861 (electronic)
ISSNL: 0138-9130 OR 01389130
bibdate: Wed Feb 21 15:50:41 MST 2018
bibtimestamp: 2018.02.21 15:50:41 MST
URL: http://link.springer.com/article/10.1007/s11192-017-2626-1
fjournal: Scientometrics
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11192
rawauthor: Yongjun Zhang and Jialin Ma and Zijian Wang and Bolun Chen and Yongtao Yu
title:
Collective topical PageRank: a model to evaluate the topic-dependent academic impact of scientific papers

rawtitle:
Collective topical {PageRank}: a model to evaluate the topic-dependent academic impact of scientific papers

entry:
@Article{Zhang:2018:CTP,
  author =       "Yongjun Zhang and Jialin Ma and Zijian Wang and Bolun
                 Chen and Yongtao Yu",
  title =        "Collective topical {PageRank}: a model to evaluate the
                 topic-dependent academic impact of scientific papers",
  journal =      j-SCIENTOMETRICS,
  volume =       "114",
  number =       "3",
  pages =        "1345--1372",
  month =        mar,
  year =         "2018",
  CODEN =        "SCNTDX",
  DOI =          "https://doi.org/10.1007/s11192-017-2626-1",
  ISSN =         "0138-9130 (print), 1588-2861 (electronic)",
  ISSN-L =       "0138-9130",
  bibdate =      "Wed Feb 21 15:50:41 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/scientometrics2010.bib",
  URL =          "http://link.springer.com/article/10.1007/s11192-017-2626-1",
  acknowledgement = ack-nhfb,
  fjournal =     "Scientometrics",
  journal-URL =  "http://link.springer.com/journal/11192",
}


bibtype: article
filename: pagerank.bib
label: Zhang:2018:SRI
author: Ziqi Zhang and Jie Gao and Fabio Ciravegna
journal: j-TKDD
volume: 12
number: 5
pages: 57:1--57:??
month: jul
monthnumber: 7
year: 2018
DOI: https://doi.org/10.1145/3201408
ISSN: 1556-4681 (print), 1556-472X (electronic) OR 15564681 (print), 1556472X (electronic)
ISSNL: 1556-4681 OR 15564681
bibdate: Tue Jan 29 17:18:46 MST 2019
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tkdd.bib
bibtimestamp: 2019.01.29 17:18:46 MST
fjournal: ACM Transactions on Knowledge Discovery from Data (TKDD)
acknowledgement: ack-nhfb
articleno: 57
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J1054
rawauthor: Ziqi Zhang and Jie Gao and Fabio Ciravegna
title:
SemRe-Rank: Improving Automatic Term Extraction by Incorporating Semantic Relatedness with Personalised PageRank

abstract:
Automatic Term Extraction (ATE) deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that there is no existing ATE methods that can consistently outperform others in any domain. This work adopts a refreshed perspective to this problem: instead of searching for such a `one-size-fit-all' solution that may never exist, we propose to develop generic methods to `enhance' existing ATE methods. We introduce SemRe-Rank, the first method based on this principle, to incorporate semantic relatedness-an often overlooked venue-into an existing ATE method to further improve its performance. SemRe-Rank incorporates word embeddings into a personalised PageRank process to compute `semantic importance' scores for candidate terms from a graph of semantically related words (nodes), which are then used to revise the scores of candidate terms computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art base ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all base methods and across all datasets, with up to 15 percentage points when measured by the Precision in the top ranked K candidate terms (the average for a set of K 's), or up to 28 percentage points in F1 measured at a K that equals to the expected real terms in the candidates (F1 in short). Compared to an alternative approach built on the well-known TextRank algorithm, SemRe-Rank can potentially outperform by up to 8 points in Precision at top K, or up to 17 points in F1.

rawabstract:
Automatic Term Extraction (ATE) deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that there is no existing ATE methods that can consistently outperform others in any domain. This work adopts a refreshed perspective to this problem: instead of searching for such a `one-size-fit-all' solution that may never exist, we propose to develop generic methods to `enhance' existing ATE methods. We introduce SemRe-Rank, the first method based on this principle, to incorporate semantic relatedness-an often overlooked venue-into an existing ATE method to further improve its performance. SemRe-Rank incorporates word embeddings into a personalised PageRank process to compute `semantic importance' scores for candidate terms from a graph of semantically related words (nodes), which are then used to revise the scores of candidate terms computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art base ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all base methods and across all datasets, with up to 15 percentage points when measured by the Precision in the top ranked K candidate terms (the average for a set of K 's), or up to 28 percentage points in F1 measured at a K that equals to the expected real terms in the candidates (F1 in short). Compared to an alternative approach built on the well-known TextRank algorithm, SemRe-Rank can potentially outperform by up to 8 points in Precision at top K, or up to 17 points in F1.

rawtitle:
{SemRe-Rank}: Improving Automatic Term Extraction by Incorporating Semantic Relatedness with Personalised {PageRank}

entry:
@Article{Zhang:2018:SRI,
  author =       "Ziqi Zhang and Jie Gao and Fabio Ciravegna",
  title =        "{SemRe-Rank}: Improving Automatic Term Extraction by
                 Incorporating Semantic Relatedness with Personalised
                 {PageRank}",
  journal =      j-TKDD,
  volume =       "12",
  number =       "5",
  pages =        "57:1--57:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3201408",
  ISSN =         "1556-4681 (print), 1556-472X (electronic)",
  ISSN-L =       "1556-4681",
  bibdate =      "Tue Jan 29 17:18:46 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tkdd.bib",
  abstract =     "Automatic Term Extraction (ATE) deals with the
                 extraction of terminology from a domain specific
                 corpus, and has long been an established research area
                 in data and knowledge acquisition. ATE remains a
                 challenging task as it is known that there is no
                 existing ATE methods that can consistently outperform
                 others in any domain. This work adopts a refreshed
                 perspective to this problem: instead of searching for
                 such a `one-size-fit-all' solution that may never
                 exist, we propose to develop generic methods to
                 `enhance' existing ATE methods. We introduce
                 SemRe-Rank, the first method based on this principle,
                 to incorporate semantic relatedness-an often overlooked
                 venue-into an existing ATE method to further improve
                 its performance. SemRe-Rank incorporates word
                 embeddings into a personalised PageRank process to
                 compute `semantic importance' scores for candidate
                 terms from a graph of semantically related words
                 (nodes), which are then used to revise the scores of
                 candidate terms computed by a base ATE algorithm.
                 Extensively evaluated with 13 state-of-the-art base ATE
                 methods on four datasets of diverse nature, it is shown
                 to have achieved widespread improvement over all base
                 methods and across all datasets, with up to 15
                 percentage points when measured by the Precision in the
                 top ranked K candidate terms (the average for a set of
                 K 's), or up to 28 percentage points in F1 measured at
                 a K that equals to the expected real terms in the
                 candidates (F1 in short). Compared to an alternative
                 approach built on the well-known TextRank algorithm,
                 SemRe-Rank can potentially outperform by up to 8 points
                 in Precision at top K, or up to 17 points in F1.",
  acknowledgement = ack-nhfb,
  articleno =    "57",
  fjournal =     "ACM Transactions on Knowledge Discovery from Data
                 (TKDD)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1054",
}


bibtype: article
filename: pagerank.bib
label: Zheng:2018:ESG
author: Long Zheng and Xiaofei Liao and Hai Jin
title: Efficient and Scalable Graph Parallel Processing With Symbolic Execution
journal: j-TACO
volume: 15
number: 1
pages: 3:1--3:??
month: apr
monthnumber: 4
year: 2018
DOI: https://doi.org/10.1145/3170434
ISSN: 1544-3566 (print), 1544-3973 (electronic) OR 15443566 (print), 15443973 (electronic)
ISSNL: 1544-3566 OR 15443566
bibdate: Tue Jan 8 17:19:59 MST 2019
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/taco.bib
bibtimestamp: 2019.01.08 17:19:59 MST
fjournal: ACM Transactions on Architecture and Code Optimization (TACO)
acknowledgement: ack-nhfb
articleno: 3
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J924
rawauthor: Long Zheng and Xiaofei Liao and Hai Jin
rawtitle: Efficient and Scalable Graph Parallel Processing With Symbolic Execution
abstract:
Existing graph processing essentially relies on the underlying iterative execution with synchronous (Sync) and/or asynchronous (Async) engine. Nevertheless, they both suffer from a wide class of inherent serialization arising from data interdependencies within a graph. In this article, we present SymGraph, a judicious graph engine with symbolic iteration that enables the parallelism of dependent computation on vertices. SymGraph allows using abstract symbolic value (instead of the concrete value) for the computation if the desired data is unavailable. To maximize the potential of symbolic iteration, we propose a chain of tailored sophisticated techniques, enabling SymGraph to scale out with a new milestone of efficiency for large-scale graph processing. We evaluate SymGraph in comparison to Sync, Async, and a hybrid of Sync and Async engines. Our results on 12 nodes show that SymGraph outperforms all three graph engines by 1.93x (vs. Sync), 1.98x (vs. Async), and 1.57x (vs. Hybrid) on average. In particular, the performance for PageRank on 32 nodes can be dramatically improved by 16.5x (vs. Sync), 23.3x (vs. Async), and 12.1x (vs. Hybrid), respectively. The efficiency of SymGraph is also validated with at least one order of magnitude improvement in contrast to three specialized graph systems (Naiad, GraphX, and PGX.D).

rawabstract:
Existing graph processing essentially relies on the underlying iterative execution with synchronous (Sync) and/or asynchronous (Async) engine. Nevertheless, they both suffer from a wide class of inherent serialization arising from data interdependencies within a graph. In this article, we present SymGraph, a judicious graph engine with symbolic iteration that enables the parallelism of dependent computation on vertices. SymGraph allows using abstract symbolic value (instead of the concrete value) for the computation if the desired data is unavailable. To maximize the potential of symbolic iteration, we propose a chain of tailored sophisticated techniques, enabling SymGraph to scale out with a new milestone of efficiency for large-scale graph processing. We evaluate SymGraph in comparison to Sync, Async, and a hybrid of Sync and Async engines. Our results on 12 nodes show that SymGraph outperforms all three graph engines by 1.93x (vs. Sync), 1.98x (vs. Async), and 1.57x (vs. Hybrid) on average. In particular, the performance for PageRank on 32 nodes can be dramatically improved by 16.5x (vs. Sync), 23.3x (vs. Async), and 12.1x (vs. Hybrid), respectively. The efficiency of SymGraph is also validated with at least one order of magnitude improvement in contrast to three specialized graph systems (Naiad, GraphX, and PGX.D).

entry:
@Article{Zheng:2018:ESG,
  author =       "Long Zheng and Xiaofei Liao and Hai Jin",
  title =        "Efficient and Scalable Graph Parallel Processing With
                 Symbolic Execution",
  journal =      j-TACO,
  volume =       "15",
  number =       "1",
  pages =        "3:1--3:??",
  month =        apr,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3170434",
  ISSN =         "1544-3566 (print), 1544-3973 (electronic)",
  ISSN-L =       "1544-3566",
  bibdate =      "Tue Jan 8 17:19:59 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/taco.bib",
  abstract =     "Existing graph processing essentially relies on the
                 underlying iterative execution with synchronous (Sync)
                 and/or asynchronous (Async) engine. Nevertheless, they
                 both suffer from a wide class of inherent serialization
                 arising from data interdependencies within a graph. In
                 this article, we present SymGraph, a judicious graph
                 engine with symbolic iteration that enables the
                 parallelism of dependent computation on vertices.
                 SymGraph allows using abstract symbolic value (instead
                 of the concrete value) for the computation if the
                 desired data is unavailable. To maximize the potential
                 of symbolic iteration, we propose a chain of tailored
                 sophisticated techniques, enabling SymGraph to scale
                 out with a new milestone of efficiency for large-scale
                 graph processing. We evaluate SymGraph in comparison to
                 Sync, Async, and a hybrid of Sync and Async engines.
                 Our results on 12 nodes show that SymGraph outperforms
                 all three graph engines by 1.93x (vs. Sync), 1.98x (vs.
                 Async), and 1.57x (vs. Hybrid) on average. In
                 particular, the performance for PageRank on 32 nodes
                 can be dramatically improved by 16.5x (vs. Sync), 23.3x
                 (vs. Async), and 12.1x (vs. Hybrid), respectively. The
                 efficiency of SymGraph is also validated with at least
                 one order of magnitude improvement in contrast to three
                 specialized graph systems (Naiad, GraphX, and PGX.D).",
  acknowledgement = ack-nhfb,
  articleno =    "3",
  fjournal =     "ACM Transactions on Architecture and Code Optimization
                 (TACO)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J924",
}


bibtype: article
filename: pagerank.bib
label: Arrigo:2019:NBP
author: Francesca Arrigo and Desmond J. Higham and Vanni Noferini
title: Non-backtracking PageRank
journal: j-J-SCI-COMPUT
volume: 80
number: 3
pages: 1419--1437
month: sep
monthnumber: 9
year: 2019
CODEN: JSCOEB
DOI: https://doi.org/10.1007/s10915-019-00981-8
ISSN: 0885-7474 (print), 1573-7691 (electronic) OR 08857474 (print), 15737691 (electronic)
ISSNL: 0885-7474 OR 08857474
bibdate: Thu May 13 07:27:54 MDT 2021
bibtimestamp: 2021.05.13 07:27:54 MDT
fjournal: Journal of Scientific Computing
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/10915
rawauthor: Francesca Arrigo and Desmond J. Higham and Vanni Noferini
rawtitle: Non-backtracking {PageRank}
bibsource:
http://link.springer.com/journal/10915/80/3; https://www.math.utah.edu/pub/tex/bib/jscicomput.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

URL:
https://link.springer.com/article/10.1007/s10915-019-00981-8; https://link.springer.com/content/pdf/10.1007/s10915-019-00981-8.pdf

entry:
@Article{Arrigo:2019:NBP,
  author =       "Francesca Arrigo and Desmond J. Higham and Vanni
                 Noferini",
  title =        "Non-backtracking {PageRank}",
  journal =      j-J-SCI-COMPUT,
  volume =       "80",
  number =       "3",
  pages =        "1419--1437",
  month =        sep,
  year =         "2019",
  CODEN =        "JSCOEB",
  DOI =          "https://doi.org/10.1007/s10915-019-00981-8",
  ISSN =         "0885-7474 (print), 1573-7691 (electronic)",
  ISSN-L =       "0885-7474",
  bibdate =      "Thu May 13 07:27:54 MDT 2021",
  bibsource =    "http://link.springer.com/journal/10915/80/3;
                 https://www.math.utah.edu/pub/tex/bib/jscicomput.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s10915-019-00981-8;
                 https://link.springer.com/content/pdf/10.1007/s10915-019-00981-8.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Scientific Computing",
  journal-URL =  "http://link.springer.com/journal/10915",
}


bibtype: incollection
filename: pagerank.bib
label: DiBucchianico:2019:MBD
title: Mathematics for big data
crossref: Pitici:2019:BWM
pages: 120--131
year: 2019
bibdate: Mon Mar 16 15:45:15 2020
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2020.03.16 15:45:15 ???
keywords: PageRank; Web data analytics
acknowledgement: ack-nhfb
rawtitle: Mathematics for big data
author:
Alessandro Di Bucchianico and Laura Iapichino and Nelly Litvak and Frank van der Meulen and Ron Wehrens

rawauthor:
Alessandro {Di Bucchianico} and Laura Iapichino and Nelly Litvak and Frank van der Meulen and Ron Wehrens

entry:
@InCollection{DiBucchianico:2019:MBD,
  author =       "Alessandro {Di Bucchianico} and Laura Iapichino and
                 Nelly Litvak and Frank van der Meulen and Ron Wehrens",
  title =        "Mathematics for big data",
  crossref =     "Pitici:2019:BWM",
  pages =        "120--131",
  year =         "2019",
  bibdate =      "Mon Mar 16 15:45:15 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  keywords =     "PageRank; Web data analytics",
}


bibtype: article
filename: pagerank.bib
label: Makkar:2019:CSF
author: Aaisha Makkar and Neeraj Kumar
journal: j-FUT-GEN-COMP-SYS
volume: 90
pages: 381--404
month: jan
monthnumber: 1
year: 2019
CODEN: FGSEVI
DOI: https://doi.org/10.1016/j.future.2018.07.046
ISSN: 0167-739X (print), 1872-7115 (electronic) OR 0167739X (print), 18727115 (electronic)
ISSNL: 0167-739X OR 0167739X
bibdate: Tue Sep 18 14:07:59 MDT 2018
bibtimestamp: 2018.09.18 14:07:59 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0167739X18305703
fjournal: Future Generation Computer Systems
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/0167739X
rawauthor: Aaisha Makkar and Neeraj Kumar
title:
Cognitive spammer: a Framework for PageRank analysis with Split by Over-sampling and Train by Under-fitting

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

rawtitle:
Cognitive spammer: a Framework for {PageRank} analysis with Split by Over-sampling and Train by Under-fitting

entry:
@Article{Makkar:2019:CSF,
  author =       "Aaisha Makkar and Neeraj Kumar",
  title =        "Cognitive spammer: a Framework for {PageRank} analysis
                 with Split by Over-sampling and Train by
                 Under-fitting",
  journal =      j-FUT-GEN-COMP-SYS,
  volume =       "90",
  number =       "??",
  pages =        "381--404",
  month =        jan,
  year =         "2019",
  CODEN =        "FGSEVI",
  DOI =          "https://doi.org/10.1016/j.future.2018.07.046",
  ISSN =         "0167-739X (print), 1872-7115 (electronic)",
  ISSN-L =       "0167-739X",
  bibdate =      "Tue Sep 18 14:07:59 MDT 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/futgencompsys.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0167739X18305703",
  acknowledgement = ack-nhfb,
  fjournal =     "Future Generation Computer Systems",
  journal-URL =  "http://www.sciencedirect.com/science/journal/0167739X",
}


bibtype: article
filename: pagerank.bib
label: Massucci:2019:MAR
author: Francesco Alessandro Massucci and Domingo Docampo
title: Measuring the academic reputation through citation networks via PageRank
journal: j-J-INFORMETRICS
volume: 13
number: 1
pages: 185--201
month: feb
monthnumber: 2
year: 2019
ISSN: 1751-1577 (print), 1875-5879 (electronic) OR 17511577 (print), 18755879 (electronic)
ISSNL: 1751-1577 OR 17511577
bibdate: Fri Feb 5 16:33:15 MST 2021
bibtimestamp: 2021.02.05 16:33:15 MST
URL: http://www.sciencedirect.com/science/article/pii/S175115771830110X
fjournal: Journal of Informetrics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/17511577/
rawauthor: Francesco Alessandro Massucci and Domingo Docampo
rawtitle: Measuring the academic reputation through citation networks via {PageRank}
entry:
@Article{Massucci:2019:MAR,
  author =       "Francesco Alessandro Massucci and Domingo Docampo",
  title =        "Measuring the academic reputation through citation
                 networks via {PageRank}",
  journal =      j-J-INFORMETRICS,
  volume =       "13",
  number =       "1",
  pages =        "185--201",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  ISSN =         "1751-1577 (print), 1875-5879 (electronic)",
  ISSN-L =       "1751-1577",
  bibdate =      "Fri Feb 5 16:33:15 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S175115771830110X",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Informetrics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/17511577/",
}


bibtype: article
filename: pagerank.bib
label: Robertson:2019:BHS
author: Stephen Robertson
title: A Brief History of Search Results Ranking
journal: j-IEEE-ANN-HIST-COMPUT
volume: 41
number: 2
pages: 22--28
month: apr
monthnumber: 4
year: 2019
CODEN: IAHCEX
DOI: https://doi.org/10.1109/MAHC.2019.2897559
ISSN: 1058-6180 (print), 1934-1547 (electronic) OR 10586180 (print), 19341547 (electronic)
ISSNL: 1058-6180 OR 10586180
bibdate: Mon Jul 8 07:40:56 2019
bibtimestamp: 2019.07.08 07:40:56 ???
fjournal: IEEE Annals of the History of Computing
acknowledgement: ack-nhfb
journalURL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=85
rawauthor: Stephen Robertson
rawtitle: A Brief History of Search Results Ranking
bibsource:
https://www.math.utah.edu/pub/tex/bib/ieeeannhistcomput.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

keywords:
alternative methods; brief history; extensive research work; History; Indexing; Information retrieval; Internet; JACM paper; learning (artificial intelligence); learning methods; ranking systems; Rankng (statistics); search engines; Search methods; search results; twentieth century; Web search; web search engines

remark:
See url https://history.computer.org/annals/dtp/ for additional notes, corrections, interviews, and photographs.

entry:
@Article{Robertson:2019:BHS,
  author =       "Stephen Robertson",
  title =        "A Brief History of Search Results Ranking",
  journal =      j-IEEE-ANN-HIST-COMPUT,
  volume =       "41",
  number =       "2",
  pages =        "22--28",
  month =        apr,
  year =         "2019",
  CODEN =        "IAHCEX",
  DOI =          "https://doi.org/10.1109/MAHC.2019.2897559",
  ISSN =         "1058-6180 (print), 1934-1547 (electronic)",
  ISSN-L =       "1058-6180",
  bibdate =      "Mon Jul 8 07:40:56 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ieeeannhistcomput.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE Annals of the History of Computing",
  journal-URL =  "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=85",
  keywords =     "alternative methods; brief history; extensive research
                 work; History; Indexing; Information retrieval;
                 Internet; JACM paper; learning (artificial
                 intelligence); learning methods; ranking systems;
                 Rankng (statistics); search engines; Search methods;
                 search results; twentieth century; Web search; web
                 search engines",
  remark =       "See \url{https://history.computer.org/annals/dtp/} for
                 additional notes, corrections, interviews, and
                 photographs.",
}


bibtype: article
filename: pagerank.bib
label: Shen:2019:DLR
title: Off-diagonal low-rank preconditioner for difficult PageRank problems
journal: j-J-COMPUT-APPL-MATH
volume: 346
pages: 456--470
day: 15
month: jan
monthnumber: 1
year: 2019
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2018.07.015
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Mon Mar 18 11:19:57 MDT 2019
bibtimestamp: 2019.03.18 11:19:57 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0377042718304357
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawtitle: Off-diagonal low-rank preconditioner for difficult {PageRank} problems
author:
Zhao-Li Shen and Ting-Zhu Huang and Bruno Carpentieri and Chun Wen and Xian-Ming Gu and Xue-Yuan Tan

rawauthor:
Zhao-Li Shen and Ting-Zhu Huang and Bruno Carpentieri and Chun Wen and Xian-Ming Gu and Xue-Yuan Tan

entry:
@Article{Shen:2019:DLR,
  author =       "Zhao-Li Shen and Ting-Zhu Huang and Bruno Carpentieri
                 and Chun Wen and Xian-Ming Gu and Xue-Yuan Tan",
  title =        "Off-diagonal low-rank preconditioner for difficult
                 {PageRank} problems",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "346",
  number =       "??",
  pages =        "456--470",
  day =          "15",
  month =        jan,
  year =         "2019",
  CODEN =        "JCAMDI",
  DOI =          "https://doi.org/10.1016/j.cam.2018.07.015",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Mon Mar 18 11:19:57 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042718304357",
  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: Shi:2019:RTP
author: Jieming Shi and Renchi Yang and Tianyuan Jin and Xiaokui Xiao and Yin Yang
title: Realtime top-$k$ Personalized PageRank over large graphs on GPUs
journal: j-PROC-VLDB-ENDOWMENT
volume: 13
number: 1
pages: 15--28
month: sep
monthnumber: 9
year: 2019
DOI: https://doi.org/10.14778/3357377.3357379
ISSN: 2150-8097 OR 21508097
bibdate: Wed Oct 2 06:49:03 MDT 2019
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbe.bib
bibtimestamp: 2019.10.02 06:49:03 MDT
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/citation.cfm?id=J1174
rawauthor: Jieming Shi and Renchi Yang and Tianyuan Jin and Xiaokui Xiao and Yin Yang
rawtitle: Realtime top-$k$ {Personalized PageRank} over large graphs on {GPUs}
abstract:
Given a graph G, a source node s in G and a positive integer k, a top- k Personalized PageRank (PPR) query returns the k nodes with the highest PPR values with respect to s, where the PPR of a node v measures its relevance from the perspective of source s. Top- k PPR processing is a fundamental task in many important applications such as web search, social networks, and graph analytics. This paper aims to answer such a query in realtime, i.e., within less than 100ms, on an Internet-scale graph with billions of edges. This is far beyond the current state of the art, due to the immense computational cost of processing a PPR query. We achieve this goal with a novel algorithm kPAR, which utilizes the massive parallel processing power of GPUs. The main challenge in designing a GPU-based PPR algorithm lies in that a GPU is mainly a parallel computation device, whereas PPR processing involves graph traversals and value propagation operations, which are inherently sequential and memory-bound. Existing scalable PPR algorithms are mostly described as single-thread CPU solutions that are resistant to parallelization. Further, they usually involve complex data structures which do not have efficient adaptations on GPUs. kPAR overcomes these problems via both novel algorithmic designs (namely, adaptive forward push and inverted random walks ) and system engineering (e.g., load balancing) to realize the potential of GPUs. Meanwhile, kPAR provides rigorous guarantees on both result quality and worst-case efficiency. Extensive experiments show that kPAR is usually 10x faster than parallel adaptations of existing methods. Notably, on a billion-edge Twitter graph, kPAR answers a top-1000 PPR query in 42.4 milliseconds.

rawabstract:
Given a graph G, a source node s \in G and a positive integer k, a top- k Personalized PageRank (PPR) query returns the k nodes with the highest PPR values with respect to s, where the PPR of a node v measures its relevance from the perspective of source s. Top- k PPR processing is a fundamental task in many important applications such as web search, social networks, and graph analytics. This paper aims to answer such a query in realtime, i.e., within less than 100ms, on an Internet-scale graph with billions of edges. This is far beyond the current state of the art, due to the immense computational cost of processing a PPR query. We achieve this goal with a novel algorithm kPAR, which utilizes the massive parallel processing power of GPUs. The main challenge in designing a GPU-based PPR algorithm lies in that a GPU is mainly a parallel computation device, whereas PPR processing involves graph traversals and value propagation operations, which are inherently sequential and memory-bound. Existing scalable PPR algorithms are mostly described as single-thread CPU solutions that are resistant to parallelization. Further, they usually involve complex data structures which do not have efficient adaptations on GPUs. kPAR overcomes these problems via both novel algorithmic designs (namely, adaptive forward push and inverted random walks ) and system engineering (e.g., load balancing) to realize the potential of GPUs. Meanwhile, kPAR provides rigorous guarantees on both result quality and worst-case efficiency. Extensive experiments show that kPAR is usually 10x faster than parallel adaptations of existing methods. Notably, on a billion-edge Twitter graph, kPAR answers a top-1000 PPR query in 42.4 milliseconds.

entry:
@Article{Shi:2019:RTP,
  author =       "Jieming Shi and Renchi Yang and Tianyuan Jin and
                 Xiaokui Xiao and Yin Yang",
  title =        "Realtime top-$k$ {Personalized PageRank} over large
                 graphs on {GPUs}",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "13",
  number =       "1",
  pages =        "15--28",
  month =        sep,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.14778/3357377.3357379",
  ISSN =         "2150-8097",
  bibdate =      "Wed Oct 2 06:49:03 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  abstract =     "Given a graph G, a source node s \in G and a positive
                 integer k, a top- k Personalized PageRank (PPR) query
                 returns the k nodes with the highest PPR values with
                 respect to s, where the PPR of a node v measures its
                 relevance from the perspective of source s. Top- k PPR
                 processing is a fundamental task in many important
                 applications such as web search, social networks, and
                 graph analytics. This paper aims to answer such a query
                 in realtime, i.e., within less than 100ms, on an
                 Internet-scale graph with billions of edges. This is
                 far beyond the current state of the art, due to the
                 immense computational cost of processing a PPR query.
                 We achieve this goal with a novel algorithm kPAR, which
                 utilizes the massive parallel processing power of GPUs.
                 The main challenge in designing a GPU-based PPR
                 algorithm lies in that a GPU is mainly a parallel
                 computation device, whereas PPR processing involves
                 graph traversals and value propagation operations,
                 which are inherently sequential and memory-bound.
                 Existing scalable PPR algorithms are mostly described
                 as single-thread CPU solutions that are resistant to
                 parallelization. Further, they usually involve complex
                 data structures which do not have efficient adaptations
                 on GPUs. kPAR overcomes these problems via both novel
                 algorithmic designs (namely, adaptive forward push and
                 inverted random walks ) and system engineering (e.g.,
                 load balancing) to realize the potential of GPUs.
                 Meanwhile, kPAR provides rigorous guarantees on both
                 result quality and worst-case efficiency. Extensive
                 experiments show that kPAR is usually 10x faster than
                 parallel adaptations of existing methods. Notably, on a
                 billion-edge Twitter graph, kPAR answers a top-1000 PPR
                 query in 42.4 milliseconds.",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "http://portal.acm.org/citation.cfm?id=J1174",
}


bibtype: article
filename: pagerank.bib
label: Tian:2019:GIO
author: Zhaolu Tian and Yong Liu and Yan Zhang and Zhongyun Liu and Maoyi Tian
title: The general inner-outer iteration method based on regular splittings for the PageRank problem
journal: j-APPL-MATH-COMP
volume: 356
pages: 479--501
day: 1
month: sep
monthnumber: 9
year: 2019
CODEN: AMHCBQ
DOI: https://doi.org/10.1016/j.amc.2019.02.066
ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
bibdate: Wed May 15 07:15:42 MDT 2019
bibtimestamp: 2019.05.15 07:15:42 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0096300319301766
fjournal: Applied Mathematics and Computation
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00963003
rawauthor: Zhaolu Tian and Yong Liu and Yan Zhang and Zhongyun Liu and Maoyi Tian
rawtitle: The general inner-outer iteration method based on regular splittings for the {PageRank} problem
entry:
@Article{Tian:2019:GIO,
  author =       "Zhaolu Tian and Yong Liu and Yan Zhang and Zhongyun
                 Liu and Maoyi Tian",
  title =        "The general inner-outer iteration method based on
                 regular splittings for the {PageRank} problem",
  journal =      j-APPL-MATH-COMP,
  volume =       "356",
  number =       "??",
  pages =        "479--501",
  day =          "1",
  month =        sep,
  year =         "2019",
  CODEN =        "AMHCBQ",
  DOI =          "https://doi.org/10.1016/j.amc.2019.02.066",
  ISSN =         "0096-3003 (print), 1873-5649 (electronic)",
  ISSN-L =       "0096-3003",
  bibdate =      "Wed May 15 07:15:42 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/applmathcomput2015.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0096300319301766",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Mathematics and Computation",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00963003",
}


bibtype: article
filename: pagerank.bib
label: Vial:2019:RCP
author: Daniel Vial and Vijay Subramanian
title: On the Role of Clustering in Personalized PageRank Estimation
journal: j-TOMPECS
volume: 4
number: 4
pages: 21:1--21:33
month: dec
monthnumber: 12
year: 2019
DOI: https://doi.org/10.1145/3366635
ISSN: 2376-3639 (print), 2376-3647 (electronic) OR 23763639 (print), 23763647 (electronic)
ISSNL: 2376-3639 OR 23763639
bibdate: Thu Mar 19 13:56:10 MDT 2020
bibtimestamp: 2020.03.19 13:56:10 MDT
URL: https://dl.acm.org/doi/abs/10.1145/3366635
fjournal: ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)
acknowledgement: ack-nhfb
articleno: 21
journalURL: https://dl.acm.org/loi/tompecs
rawauthor: Daniel Vial and Vijay Subramanian
rawtitle: On the Role of Clustering in Personalized {PageRank} Estimation
bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tompecs.bib

abstract:
Personalized PageRank (PPR) is a measure of the importance of a node from the perspective of another (we call these nodes the target and the source, respectively). PPR has been used in many applications, such as offering a Twitter user (the source) recommendations of whom to follow (targets deemed important by PPR); additionally, PPR has been used in graph-theoretic problems such as community detection. However, computing PPR is infeasible for large networks like Twitter, so efficient estimation algorithms are necessary.par In this work, we analyze the relationship between PPR estimation complexity and clustering. First, we devise algorithms to estimate PPR for many source/target pairs. In particular, we propose an enhanced version of the existing single pair estimator Bidirectional-PPR that is more useful as a primitive for many pair estimation. We then show that the common underlying graph can be leveraged to efficiently and jointly estimate PPR for many pairs rather than treating each pair separately using the primitive algorithm. Next, we show the complexity of our joint estimation scheme relates closely to the degree of clustering among the sources and targets at hand, indicating that estimating PPR for many pairs is easier when clustering occurs. Finally, we consider estimating PPR when several machines are available for parallel computation, devising a method that leverages our clustering findings, specifically the quantities computed in situ, to assign tasks to machines in a manner that reduces computation time. This demonstrates that the relationship between complexity and clustering has important consequences in a practical distributed setting.

rawabstract:
Personalized PageRank (PPR) is a measure of the importance of a node from the perspective of another (we call these nodes the target and the source, respectively). PPR has been used in many applications, such as offering a Twitter user (the source) recommendations of whom to follow (targets deemed important by PPR); additionally, PPR has been used in graph-theoretic problems such as community detection. However, computing PPR is infeasible for large networks like Twitter, so efficient estimation algorithms are necessary.\par In this work, we analyze the relationship between PPR estimation complexity and clustering. First, we devise algorithms to estimate PPR for many source/target pairs. In particular, we propose an enhanced version of the existing single pair estimator Bidirectional-PPR that is more useful as a primitive for many pair estimation. We then show that the common underlying graph can be leveraged to efficiently and jointly estimate PPR for many pairs rather than treating each pair separately using the primitive algorithm. Next, we show the complexity of our joint estimation scheme relates closely to the degree of clustering among the sources and targets at hand, indicating that estimating PPR for many pairs is easier when clustering occurs. Finally, we consider estimating PPR when several machines are available for parallel computation, devising a method that leverages our clustering findings, specifically the quantities computed in situ, to assign tasks to machines in a manner that reduces computation time. This demonstrates that the relationship between complexity and clustering has important consequences in a practical distributed setting.

entry:
@Article{Vial:2019:RCP,
  author =       "Daniel Vial and Vijay Subramanian",
  title =        "On the Role of Clustering in Personalized {PageRank}
                 Estimation",
  journal =      j-TOMPECS,
  volume =       "4",
  number =       "4",
  pages =        "21:1--21:33",
  month =        dec,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3366635",
  ISSN =         "2376-3639 (print), 2376-3647 (electronic)",
  ISSN-L =       "2376-3639",
  bibdate =      "Thu Mar 19 13:56:10 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tompecs.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3366635",
  abstract =     "Personalized PageRank (PPR) is a measure of the
                 importance of a node from the perspective of another
                 (we call these nodes the target and the source,
                 respectively). PPR has been used in many applications,
                 such as offering a Twitter user (the source)
                 recommendations of whom to follow (targets deemed
                 important by PPR); additionally, PPR has been used in
                 graph-theoretic problems such as community detection.
                 However, computing PPR is infeasible for large networks
                 like Twitter, so efficient estimation algorithms are
                 necessary.\par

                 In this work, we analyze the relationship between PPR
                 estimation complexity and clustering. First, we devise
                 algorithms to estimate PPR for many source/target
                 pairs. In particular, we propose an enhanced version of
                 the existing single pair estimator Bidirectional-PPR
                 that is more useful as a primitive for many pair
                 estimation. We then show that the common underlying
                 graph can be leveraged to efficiently and jointly
                 estimate PPR for many pairs rather than treating each
                 pair separately using the primitive algorithm. Next, we
                 show the complexity of our joint estimation scheme
                 relates closely to the degree of clustering among the
                 sources and targets at hand, indicating that estimating
                 PPR for many pairs is easier when clustering occurs.
                 Finally, we consider estimating PPR when several
                 machines are available for parallel computation,
                 devising a method that leverages our clustering
                 findings, specifically the quantities computed in situ,
                 to assign tasks to machines in a manner that reduces
                 computation time. This demonstrates that the
                 relationship between complexity and clustering has
                 important consequences in a practical distributed
                 setting.",
  acknowledgement = ack-nhfb,
  articleno =    "21",
  fjournal =     "ACM Transactions on Modeling and Performance
                 Evaluation of Computing Systems (TOMPECS)",
  journal-URL =  "https://dl.acm.org/loi/tompecs",
}


bibtype: article
filename: pagerank.bib
label: Vial:2019:SRP
author: Daniel Vial and Vijay Subramanian
title: A Structural Result for Personalized PageRank and its Algorithmic Consequences
journal: j-SIGMETRICS
volume: 47
number: 1
pages: 39--40
month: dec
monthnumber: 12
year: 2019
DOI: https://doi.org/10.1145/3376930.3376956
ISSN: 0163-5999 (print), 1557-9484 (electronic) OR 01635999 (print), 15579484 (electronic)
ISSNL: 0163-5999 OR 01635999
bibdate: Mon Jan 27 06:15:26 MST 2020
bibtimestamp: 2020.01.27 06:15:26 MST
URL: https://dl.acm.org/doi/abs/10.1145/3376930.3376956
fjournal: ACM SIGMETRICS Performance Evaluation Review
acknowledgement: ack-nhfb
journalURL: https://dl.acm.org/loi/sigmetrics
rawauthor: Daniel Vial and Vijay Subramanian
rawtitle: A Structural Result for Personalized {PageRank} and its Algorithmic Consequences
bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/sigmetrics.bib

abstract:
Many natural and man-made systems can be represented as graphs, sets of objects (called nodes) and pairwise relations between these objects (called edges). These include the brain, which contains neurons (nodes) that exchange signals through chemical ldots 

rawabstract:
Many natural and man-made systems can be represented as graphs, sets of objects (called nodes) and pairwise relations between these objects (called edges). These include the brain, which contains neurons (nodes) that exchange signals through chemical \ldots{}

entry:
@Article{Vial:2019:SRP,
  author =       "Daniel Vial and Vijay Subramanian",
  title =        "A Structural Result for Personalized {PageRank} and
                 its Algorithmic Consequences",
  journal =      j-SIGMETRICS,
  volume =       "47",
  number =       "1",
  pages =        "39--40",
  month =        dec,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3376930.3376956",
  ISSN =         "0163-5999 (print), 1557-9484 (electronic)",
  ISSN-L =       "0163-5999",
  bibdate =      "Mon Jan 27 06:15:26 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/sigmetrics.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3376930.3376956",
  abstract =     "Many natural and man-made systems can be represented
                 as graphs, sets of objects (called nodes) and pairwise
                 relations between these objects (called edges). These
                 include the brain, which contains neurons (nodes) that
                 exchange signals through chemical \ldots{}",
  acknowledgement = ack-nhfb,
  fjournal =     "ACM SIGMETRICS Performance Evaluation Review",
  journal-URL =  "https://dl.acm.org/loi/sigmetrics",
}


bibtype: article
filename: pagerank.bib
label: Wang:2019:EAA
title: Efficient Algorithms for Approximate Single-Source Personalized PageRank Queries
journal: j-TODS
volume: 44
number: 4
pages: 18:1--18:??
month: oct
monthnumber: 10
year: 2019
CODEN: ATDSD3
DOI: https://doi.org/10.1145/3360902
ISSN: 0362-5915 (print), 1557-4644 (electronic) OR 03625915 (print), 15574644 (electronic)
ISSNL: 0362-5915 OR 03625915
bibdate: Tue Oct 29 10:55:21 MDT 2019
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tods.bib
bibtimestamp: 2019.10.29 10:55:21 MDT
URL: https://dl.acm.org/ft_gateway.cfm?id=3360902
fjournal: ACM Transactions on Database Systems
acknowledgement: ack-nhfb
articleno: 18
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J777
rawtitle: Efficient Algorithms for Approximate Single-Source Personalized {PageRank} Queries
author:
Sibo Wang and Renchi Yang and Runhui Wang and Xiaokui Xiao and Zhewei Wei and Wenqing Lin and Yin Yang and Nan Tang

abstract:
Given a graph G, a source node s, and a target node t, the personalized PageRank ( PPR ) of t with respect to s is the probability that a random walk starting from s terminates at t. An important variant of the PPR query is single-source PPR ( SSPPR ), which enumerates all nodes in G and returns the top- k nodes with the highest PPR values with respect to a given source s. PPR in general and SSPPR in particular have important applications in web search and social networks, e.g., in Twitter's Who-To-Follow recommendation service. However, PPR computation is known to be expensive on large graphs and resistant to indexing. Consequently, previous solutions either use heuristics, which do not guarantee result quality, or rely on the strong computing power of modern data centers, which is costly. Motivated by this, we propose effective index-free and index-based algorithms for approximate PPR processing, with rigorous guarantees on result quality. We first present FORA, an approximate SSPPR solution that combines two existing methods-Forward Push (which is fast but does not guarantee quality) and Monte Carlo Random Walk (accurate but slow)-in a simple and yet non-trivial way, leading to both high accuracy and efficiency. Further, FORA includes a simple and effective indexing scheme, as well as a module for top- k selection with high pruning power. Extensive experiments demonstrate that the proposed solutions are orders of magnitude more efficient than their respective competitors. Notably, on a billion-edge Twitter dataset, FORA answers a top-500 approximate SSPPR query within 1s, using a single commodity server.

rawabstract:
Given a graph G, a source node s, and a target node t, the personalized PageRank ( PPR ) of t with respect to s is the probability that a random walk starting from s terminates at t. An important variant of the PPR query is single-source PPR ( SSPPR ), which enumerates all nodes in G and returns the top- k nodes with the highest PPR values with respect to a given source s. PPR in general and SSPPR in particular have important applications in web search and social networks, e.g., in Twitter's Who-To-Follow recommendation service. However, PPR computation is known to be expensive on large graphs and resistant to indexing. Consequently, previous solutions either use heuristics, which do not guarantee result quality, or rely on the strong computing power of modern data centers, which is costly. Motivated by this, we propose effective index-free and index-based algorithms for approximate PPR processing, with rigorous guarantees on result quality. We first present FORA, an approximate SSPPR solution that combines two existing methods-Forward Push (which is fast but does not guarantee quality) and Monte Carlo Random Walk (accurate but slow)-in a simple and yet non-trivial way, leading to both high accuracy and efficiency. Further, FORA includes a simple and effective indexing scheme, as well as a module for top- k selection with high pruning power. Extensive experiments demonstrate that the proposed solutions are orders of magnitude more efficient than their respective competitors. Notably, on a billion-edge Twitter dataset, FORA answers a top-500 approximate SSPPR query within 1s, using a single commodity server.

rawauthor:
Sibo Wang and Renchi Yang and Runhui Wang and Xiaokui Xiao and Zhewei Wei and Wenqing Lin and Yin Yang and Nan Tang

entry:
@Article{Wang:2019:EAA,
  author =       "Sibo Wang and Renchi Yang and Runhui Wang and Xiaokui
                 Xiao and Zhewei Wei and Wenqing Lin and Yin Yang and
                 Nan Tang",
  title =        "Efficient Algorithms for Approximate Single-Source
                 Personalized {PageRank} Queries",
  journal =      j-TODS,
  volume =       "44",
  number =       "4",
  pages =        "18:1--18:??",
  month =        oct,
  year =         "2019",
  CODEN =        "ATDSD3",
  DOI =          "https://doi.org/10.1145/3360902",
  ISSN =         "0362-5915 (print), 1557-4644 (electronic)",
  ISSN-L =       "0362-5915",
  bibdate =      "Tue Oct 29 10:55:21 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tods.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3360902",
  abstract =     "Given a graph G, a source node s, and a target node t,
                 the personalized PageRank ( PPR ) of t with respect to
                 s is the probability that a random walk starting from s
                 terminates at t. An important variant of the PPR query
                 is single-source PPR ( SSPPR ), which enumerates all
                 nodes in G and returns the top- k nodes with the
                 highest PPR values with respect to a given source s.
                 PPR in general and SSPPR in particular have important
                 applications in web search and social networks, e.g.,
                 in Twitter's Who-To-Follow recommendation service.
                 However, PPR computation is known to be expensive on
                 large graphs and resistant to indexing. Consequently,
                 previous solutions either use heuristics, which do not
                 guarantee result quality, or rely on the strong
                 computing power of modern data centers, which is
                 costly. Motivated by this, we propose effective
                 index-free and index-based algorithms for approximate
                 PPR processing, with rigorous guarantees on result
                 quality. We first present FORA, an approximate SSPPR
                 solution that combines two existing methods-Forward
                 Push (which is fast but does not guarantee quality) and
                 Monte Carlo Random Walk (accurate but slow)-in a simple
                 and yet non-trivial way, leading to both high accuracy
                 and efficiency. Further, FORA includes a simple and
                 effective indexing scheme, as well as a module for top-
                 k selection with high pruning power. Extensive
                 experiments demonstrate that the proposed solutions are
                 orders of magnitude more efficient than their
                 respective competitors. Notably, on a billion-edge
                 Twitter dataset, FORA answers a top-500 approximate
                 SSPPR query within 1s, using a single commodity
                 server.",
  acknowledgement = ack-nhfb,
  articleno =    "18",
  fjournal =     "ACM Transactions on Database Systems",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J777",
}


bibtype: article
filename: pagerank.bib
label: Wang:2019:PAS
author: Runhui Wang and Sibo Wang and Xiaofang Zhou
title: Parallelizing approximate single-source personalized PageRank queries on shared memory
journal: j-VLDB-J
volume: 28
number: 6
pages: 923--940
month: dec
monthnumber: 12
year: 2019
CODEN: VLDBFR
DOI: https://doi.org/10.1007/s00778-019-00576-7
ISSN: 1066-8888 (print), 0949-877X (electronic) OR 10668888 (print), 0949877X (electronic)
ISSNL: 1066-8888 OR 10668888
bibdate: Thu Mar 19 17:10:21 MDT 2020
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbj.bib
bibtimestamp: 2020.03.19 17:10:21 MDT
URL: http://link.springer.com/article/10.1007/s00778-019-00576-7
fjournal: VLDB Journal: Very Large Data Bases
acknowledgement: ack-nhfb
journalURL: http://portal.acm.org/toc.cfm?id=J869
rawauthor: Runhui Wang and Sibo Wang and Xiaofang Zhou
rawtitle: Parallelizing approximate single-source personalized {PageRank} queries on shared memory
entry:
@Article{Wang:2019:PAS,
  author =       "Runhui Wang and Sibo Wang and Xiaofang Zhou",
  title =        "Parallelizing approximate single-source personalized
                 {PageRank} queries on shared memory",
  journal =      j-VLDB-J,
  volume =       "28",
  number =       "6",
  pages =        "923--940",
  month =        dec,
  year =         "2019",
  CODEN =        "VLDBFR",
  DOI =          "https://doi.org/10.1007/s00778-019-00576-7",
  ISSN =         "1066-8888 (print), 0949-877X (electronic)",
  ISSN-L =       "1066-8888",
  bibdate =      "Thu Mar 19 17:10:21 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbj.bib",
  URL =          "http://link.springer.com/article/10.1007/s00778-019-00576-7",
  acknowledgement = ack-nhfb,
  fjournal =     "VLDB Journal: Very Large Data Bases",
  journal-URL =  "http://portal.acm.org/toc.cfm?id=J869",
}


bibtype: article
filename: pagerank.bib
label: Yao:2019:TBR
author: Xin Yao and Yizhu Zou and Zhigang Chen and Ming Zhao and Qin Liu
title: Topic-based rank search with verifiable social data outsourcing
journal: j-J-PAR-DIST-COMP
volume: 134
pages: 1--12
month: dec
monthnumber: 12
year: 2019
CODEN: JPDCER
ISSN: 0743-7315 (print), 1096-0848 (electronic) OR 07437315 (print), 10960848 (electronic)
ISSNL: 0743-7315 OR 07437315
bibdate: Wed Mar 18 09:26:10 MDT 2020
bibtimestamp: 2020.03.18 09:26:10 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0743731519300322
fjournal: Journal of Parallel and Distributed Computing
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/07437315
rawauthor: Xin Yao and Yizhu Zou and Zhigang Chen and Ming Zhao and Qin Liu
rawtitle: Topic-based rank search with verifiable social data outsourcing
bibsource:
https://www.math.utah.edu/pub/tex/bib/jpardistcomp.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Yao:2019:TBR,
  author =       "Xin Yao and Yizhu Zou and Zhigang Chen and Ming Zhao
                 and Qin Liu",
  title =        "Topic-based rank search with verifiable social data
                 outsourcing",
  journal =      j-J-PAR-DIST-COMP,
  volume =       "134",
  number =       "??",
  pages =        "1--12",
  month =        dec,
  year =         "2019",
  CODEN =        "JPDCER",
  ISSN =         "0743-7315 (print), 1096-0848 (electronic)",
  ISSN-L =       "0743-7315",
  bibdate =      "Wed Mar 18 09:26:10 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jpardistcomp.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0743731519300322",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Parallel and Distributed Computing",
  journal-URL =  "http://www.sciencedirect.com/science/journal/07437315",
}


bibtype: article
filename: pagerank.bib
label: Yu:2019:EPP
author: Weiren Yu and Julie McCann and Chengyuan Zhang
title: Efficient Pairwise Penetrating-rank Similarity Retrieval
journal: j-TWEB
volume: 13
number: 4
pages: 21:1--21:??
month: dec
monthnumber: 12
year: 2019
DOI: https://doi.org/10.1145/3368616
ISSN: 1559-1131 (print), 1559-114X (electronic) OR 15591131 (print), 1559114X (electronic)
ISSNL: 1559-1131 OR 15591131
bibdate: Sat Dec 21 07:39:03 MST 2019
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tweb.bib
bibtimestamp: 2019.12.21 07:39:03 MST
fjournal: ACM Transactions on the Web (TWEB)
acknowledgement: ack-nhfb
articleno: 21
journalURL: http://portal.acm.org/browse_dl.cfm?idx=J1062
rawauthor: Weiren Yu and Julie McCann and Chengyuan Zhang
rawtitle: Efficient Pairwise Penetrating-rank Similarity Retrieval
abstract:
Many web applications demand a measure of similarity between two entities, such as collaborative filtering, web document ranking, linkage prediction, and anomaly detection. P-Rank (Penetrating-Rank) has been accepted as a promising graph-based similarity measure, as it provides a comprehensive way of encoding both incoming and outgoing links into assessment. However, the existing method to compute P-Rank is iterative in nature and rather cost-inhibitive. Moreover, the accuracy estimate and stability issues for P-Rank computation have not been addressed. In this article, we consider the optimization techniques for P-Rank search that encompasses its accuracy, stability, and computational efficiency. (1) The accuracy estimation is provided for P-Rank iterations, with the aim to find out the number of iterations, $k$, required to guarantee a desired accuracy. (2) A rigorous bound on the condition number of P-Rank is obtained for stability analysis. Based on this bound, it can be shown that P-Rank is stable and well-conditioned when the damping factors are chosen to be suitably small. (3) Two matrix-based algorithms, applicable to digraphs and undirected graphs, are, respectively, devised for efficient P-Rank computation, which improves the computational time from $ O(k n^3) $ to $ O(upsilon n^2 + upsilon ^6) $ for digraphs, and to $ O(upsilon n^2) $ for undirected graphs, where $n$ is the number of vertices in the graph, and $ upsilon (ll n)$ is the target rank of the graph. Moreover, our proposed algorithms can significantly reduce the memory space of P-Rank computations from $ O(n^2) $ to $ O(upsilon n + upsilon ^4) $ for digraphs, and to $ O(upsilon n) $ for undirected graphs, respectively. Finally, extensive experiments on real-world and synthetic datasets demonstrate the usefulness and efficiency of the proposed techniques for P-Rank similarity assessment on various networks.

rawabstract:
Many web applications demand a measure of similarity between two entities, such as collaborative filtering, web document ranking, linkage prediction, and anomaly detection. P-Rank (Penetrating-Rank) has been accepted as a promising graph-based similarity measure, as it provides a comprehensive way of encoding both incoming and outgoing links into assessment. However, the existing method to compute P-Rank is iterative in nature and rather cost-inhibitive. Moreover, the accuracy estimate and stability issues for P-Rank computation have not been addressed. In this article, we consider the optimization techniques for P-Rank search that encompasses its accuracy, stability, and computational efficiency. (1) The accuracy estimation is provided for P-Rank iterations, with the aim to find out the number of iterations, $k$, required to guarantee a desired accuracy. (2) A rigorous bound on the condition number of P-Rank is obtained for stability analysis. Based on this bound, it can be shown that P-Rank is stable and well-conditioned when the damping factors are chosen to be suitably small. (3) Two matrix-based algorithms, applicable to digraphs and undirected graphs, are, respectively, devised for efficient P-Rank computation, which improves the computational time from $ O(k n^3) $ to $ O(\upsilon n^2 + \upsilon^6) $ for digraphs, and to $ O(\upsilon n^2) $ for undirected graphs, where $n$ is the number of vertices in the graph, and $ \upsilon (\ll n)$ is the target rank of the graph. Moreover, our proposed algorithms can significantly reduce the memory space of P-Rank computations from $ O(n^2) $ to $ O(\upsilon n + \upsilon^4) $ for digraphs, and to $ O(\upsilon n) $ for undirected graphs, respectively. Finally, extensive experiments on real-world and synthetic datasets demonstrate the usefulness and efficiency of the proposed techniques for P-Rank similarity assessment on various networks.

entry:
@Article{Yu:2019:EPP,
  author =       "Weiren Yu and Julie McCann and Chengyuan Zhang",
  title =        "Efficient Pairwise Penetrating-rank Similarity
                 Retrieval",
  journal =      j-TWEB,
  volume =       "13",
  number =       "4",
  pages =        "21:1--21:??",
  month =        dec,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3368616",
  ISSN =         "1559-1131 (print), 1559-114X (electronic)",
  ISSN-L =       "1559-1131",
  bibdate =      "Sat Dec 21 07:39:03 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tweb.bib",
  abstract =     "Many web applications demand a measure of similarity
                 between two entities, such as collaborative filtering,
                 web document ranking, linkage prediction, and anomaly
                 detection. P-Rank (Penetrating-Rank) has been accepted
                 as a promising graph-based similarity measure, as it
                 provides a comprehensive way of encoding both incoming
                 and outgoing links into assessment. However, the
                 existing method to compute P-Rank is iterative in
                 nature and rather cost-inhibitive. Moreover, the
                 accuracy estimate and stability issues for P-Rank
                 computation have not been addressed. In this article,
                 we consider the optimization techniques for P-Rank
                 search that encompasses its accuracy, stability, and
                 computational efficiency. (1) The accuracy estimation
                 is provided for P-Rank iterations, with the aim to find
                 out the number of iterations, $k$, required to
                 guarantee a desired accuracy. (2) A rigorous bound on
                 the condition number of P-Rank is obtained for
                 stability analysis. Based on this bound, it can be
                 shown that P-Rank is stable and well-conditioned when
                 the damping factors are chosen to be suitably small.
                 (3) Two matrix-based algorithms, applicable to digraphs
                 and undirected graphs, are, respectively, devised for
                 efficient P-Rank computation, which improves the
                 computational time from $ O(k n^3) $ to $ O(\upsilon
                 n^2 + \upsilon^6) $ for digraphs, and to $ O(\upsilon
                 n^2) $ for undirected graphs, where $n$ is the number
                 of vertices in the graph, and $ \upsilon (\ll n)$ is
                 the target rank of the graph. Moreover, our proposed
                 algorithms can significantly reduce the memory space of
                 P-Rank computations from $ O(n^2) $ to $ O(\upsilon n +
                 \upsilon^4) $ for digraphs, and to $ O(\upsilon n) $
                 for undirected graphs, respectively. Finally, extensive
                 experiments on real-world and synthetic datasets
                 demonstrate the usefulness and efficiency of the
                 proposed techniques for P-Rank similarity assessment on
                 various networks.",
  acknowledgement = ack-nhfb,
  articleno =    "21",
  fjournal =     "ACM Transactions on the Web (TWEB)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1062",
}


bibtype: article
filename: pagerank.bib
label: Chen:2020:TSM
author: Fan Chen and Yini Zhang and Karl Rohe
title: Targeted sampling from massive block model graphs with personalized PageRank
journal: j-J-R-STAT-SOC-SER-B-STAT-METHODOL
volume: 82
number: 1
pages: 99--126
month: feb
monthnumber: 2
year: 2020
CODEN: JSTBAJ
DOI: https://doi.org/10.1111/rssb.12349
ISSN: 1369-7412 (print), 1467-9868 (electronic) OR 13697412 (print), 14679868 (electronic)
ISSNL: 1369-7412 OR 13697412
bibdate: Tue Jul 14 18:37:39 MDT 2020
bibtimestamp: 2020.07.14 18:37:39 MDT
fjournal: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
acknowledgement: ack-nhfb
ajournal: J. R. Stat. Soc., Ser. B Stat. Methodol.
journalURL: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9868
onlinedate: 31 December 2019
rawauthor: Fan Chen and Yini Zhang and Karl Rohe
rawtitle: Targeted sampling from massive block model graphs with personalized {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/jrss-b.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Chen:2020:TSM,
  author =       "Fan Chen and Yini Zhang and Karl Rohe",
  title =        "Targeted sampling from massive block model graphs with
                 personalized {PageRank}",
  journal =      j-J-R-STAT-SOC-SER-B-STAT-METHODOL,
  volume =       "82",
  number =       "1",
  pages =        "99--126",
  month =        feb,
  year =         "2020",
  CODEN =        "JSTBAJ",
  DOI =          "https://doi.org/10.1111/rssb.12349",
  ISSN =         "1369-7412 (print), 1467-9868 (electronic)",
  ISSN-L =       "1369-7412",
  bibdate =      "Tue Jul 14 18:37:39 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jrss-b.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "J. R. Stat. Soc., Ser. B Stat. Methodol.",
  fjournal =     "Journal of the Royal Statistical Society: Series B
                 (Statistical Methodology)",
  journal-URL =  "http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9868",
  onlinedate =   "31 December 2019",
}


bibtype: article
filename: pagerank.bib
label: Cipolla:2020:EMF
author: Stefano Cipolla and Michela Redivo-Zaglia and Francesco Tudisco
title: Extrapolation methods for fixed-point multilinear PageRank computations
journal: j-NUM-LIN-ALG-APPL
volume: 27
number: 2
pages: e2280:1--e2280:??
month: mar
monthnumber: 3
year: 2020
CODEN: NLAAEM
DOI: https://doi.org/10.1002/nla.2280
ISSN: 1070-5325 (print), 1099-1506 (electronic) OR 10705325 (print), 10991506 (electronic)
ISSNL: 1070-5325 OR 10705325
bibdate: Wed May 27 12:52:44 MDT 2020
bibtimestamp: 2020.05.27 12:52:44 MDT
fjournal: Numerical Linear Algebra with Applications
acknowledgement: ack-nhfb
journalURL: http://www3.interscience.wiley.com/cgi-bin/jhome/5957
onlinedate: 03 January 2020
rawauthor: Stefano Cipolla and Michela Redivo-Zaglia and Francesco Tudisco
rawtitle: Extrapolation methods for fixed-point multilinear {PageRank} computations
bibsource:
https://www.math.utah.edu/pub/tex/bib/numlinaa.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Cipolla:2020:EMF,
  author =       "Stefano Cipolla and Michela Redivo-Zaglia and
                 Francesco Tudisco",
  title =        "Extrapolation methods for fixed-point multilinear
                 {PageRank} computations",
  journal =      j-NUM-LIN-ALG-APPL,
  volume =       "27",
  number =       "2",
  pages =        "e2280:1--e2280:??",
  month =        mar,
  year =         "2020",
  CODEN =        "NLAAEM",
  DOI =          "https://doi.org/10.1002/nla.2280",
  ISSN =         "1070-5325 (print), 1099-1506 (electronic)",
  ISSN-L =       "1070-5325",
  bibdate =      "Wed May 27 12:52:44 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Numerical Linear Algebra with Applications",
  journal-URL =  "http://www3.interscience.wiley.com/cgi-bin/jhome/5957",
  onlinedate =   "03 January 2020",
}


bibtype: article
filename: pagerank.bib
label: Garavaglia:2020:LWC
author: Alessandro Garavaglia and Remco van der Hofstad and Nelly Litvak
title: Local weak convergence for PageRank
journal: j-ANN-APPL-PROBAB
volume: 30
number: 1
pages: 40--79
month: feb
monthnumber: 2
year: 2020
ISSN: 1050-5164 (print), 2168-8737 (electronic) OR 10505164 (print), 21688737 (electronic)
ISSNL: 1050-5164 OR 10505164
bibdate: Tue Jul 14 17:01:23 MDT 2020
bibtimestamp: 2020.07.14 17:01:23 MDT
URL: https://projecteuclid.org/euclid.aoap/1582621219
fjournal: Annals of Applied Probability
acknowledgement: ack-nhfb
ajournal: Ann. Appl. Probab.
journalURL: http://projecteuclid.org/all/euclid.aoap/; http://www.jstor.org/journals/10505164.html
rawauthor: Alessandro Garavaglia and Remco van der Hofstad and Nelly Litvak
rawtitle: Local weak convergence for {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/annapplprobab.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Garavaglia:2020:LWC,
  author =       "Alessandro Garavaglia and Remco van der Hofstad and
                 Nelly Litvak",
  title =        "Local weak convergence for {PageRank}",
  journal =      j-ANN-APPL-PROBAB,
  volume =       "30",
  number =       "1",
  pages =        "40--79",
  month =        feb,
  year =         "2020",
  CODEN =        "????",
  ISSN =         "1050-5164 (print), 2168-8737 (electronic)",
  ISSN-L =       "1050-5164",
  bibdate =      "Tue Jul 14 17:01:23 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/annapplprobab.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://projecteuclid.org/euclid.aoap/1582621219",
  acknowledgement = ack-nhfb,
  ajournal =     "Ann. Appl. Probab.",
  fjournal =     "Annals of Applied Probability",
  journal-URL =  "http://projecteuclid.org/all/euclid.aoap/;
                 http://www.jstor.org/journals/10505164.html",
}


bibtype: article
filename: pagerank.bib
label: Grutzmacher:2020:APC
author: Thomas Grutzmacher and Terry Cojean and Goran Flegar and Hartwig Anzt and Enrique S. Quintana-Orti
title: Acceleration of PageRank with Customized Precision Based on Mantissa Segmentation
journal: j-TOPC
volume: 7
number: 1
pages: 4:1--4:19
month: apr
monthnumber: 4
year: 2020
DOI: https://doi.org/10.1145/3380934
ISSN: 2329-4949 (print), 2329-4957 (electronic) OR 23294949 (print), 23294957 (electronic)
ISSNL: 2329-4949 OR 23294949
bibdate: Mon Apr 6 08:56:55 MDT 2020
bibtimestamp: 2020.04.06 08:56:55 MDT
URL: https://dl.acm.org/doi/abs/10.1145/3380934
fjournal: ACM Transactions on Parallel Computing
acknowledgement: ack-nhfb
articleno: 4
journalURL: https://dl.acm.org/loi/topc
rawtitle: Acceleration of {PageRank} with Customized Precision Based on Mantissa Segmentation
bibsource:
https://www.math.utah.edu/pub/tex/bib/fparith.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/topc.bib

abstract:
We describe the application of a communication-reduction technique for the PageRank algorithm that dynamically adapts the precision of the data access to the numerical requirements of the algorithm as the iteration converges. Our variable-precision strategy, using a customized precision format based on mantissa segmentation (CPMS), abandons the IEEE 754 single- and double-precision number representation formats employed in the standard implementation of PageRank, and instead handles the data in memory using a customized floating-point format. The customized format enables fast data access in different accuracy, prevents overflow/underflow by preserving the IEEE 754 double-precision exponent, and efficiently avoids data duplication, since all bits of the original IEEE 754 double-precision mantissa are preserved in memory, but re-organized for efficient reduced precision access. With this approach, the truncated values (omitting significand bits), as well as the original IEEE double-precision values, can be retrieved without duplicating the data in different formats.par Our numerical experiments on an NVIDIA V100 GPU (Volta architecture) and a server equipped with two Intel Xeon Platinum 8168 CPUs (48 cores in total) expose that, compared with a standard IEEE double-precision implementation, the CPMS-based PageRank completes about 10% faster if high-accuracy output is needed, and about 30% faster if reduced output accuracy is acceptable.

rawabstract:
We describe the application of a communication-reduction technique for the PageRank algorithm that dynamically adapts the precision of the data access to the numerical requirements of the algorithm as the iteration converges. Our variable-precision strategy, using a customized precision format based on mantissa segmentation (CPMS), abandons the IEEE 754 single- and double-precision number representation formats employed in the standard implementation of PageRank, and instead handles the data in memory using a customized floating-point format. The customized format enables fast data access in different accuracy, prevents overflow/underflow by preserving the IEEE 754 double-precision exponent, and efficiently avoids data duplication, since all bits of the original IEEE 754 double-precision mantissa are preserved in memory, but re-organized for efficient reduced precision access. With this approach, the truncated values (omitting significand bits), as well as the original IEEE double-precision values, can be retrieved without duplicating the data in different formats.\par Our numerical experiments on an NVIDIA V100 GPU (Volta architecture) and a server equipped with two Intel Xeon Platinum 8168 CPUs (48 cores in total) expose that, compared with a standard IEEE double-precision implementation, the CPMS-based PageRank completes about 10\% faster if high-accuracy output is needed, and about 30\% faster if reduced output accuracy is acceptable.

rawauthor:
Thomas Gr{\"u}tzmacher and Terry Cojean and Goran Flegar and Hartwig Anzt and Enrique S. Quintana-Ort{\'\i}

entry:
@Article{Grutzmacher:2020:APC,
  author =       "Thomas Gr{\"u}tzmacher and Terry Cojean and Goran
                 Flegar and Hartwig Anzt and Enrique S.
                 Quintana-Ort{\'\i}",
  title =        "Acceleration of {PageRank} with Customized Precision
                 Based on Mantissa Segmentation",
  journal =      j-TOPC,
  volume =       "7",
  number =       "1",
  pages =        "4:1--4:19",
  month =        apr,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3380934",
  ISSN =         "2329-4949 (print), 2329-4957 (electronic)",
  ISSN-L =       "2329-4949",
  bibdate =      "Mon Apr 6 08:56:55 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/fparith.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/topc.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3380934",
  abstract =     "We describe the application of a
                 communication-reduction technique for the PageRank
                 algorithm that dynamically adapts the precision of the
                 data access to the numerical requirements of the
                 algorithm as the iteration converges. Our
                 variable-precision strategy, using a customized
                 precision format based on mantissa segmentation (CPMS),
                 abandons the IEEE 754 single- and double-precision
                 number representation formats employed in the standard
                 implementation of PageRank, and instead handles the
                 data in memory using a customized floating-point
                 format. The customized format enables fast data access
                 in different accuracy, prevents overflow/underflow by
                 preserving the IEEE 754 double-precision exponent, and
                 efficiently avoids data duplication, since all bits of
                 the original IEEE 754 double-precision mantissa are
                 preserved in memory, but re-organized for efficient
                 reduced precision access. With this approach, the
                 truncated values (omitting significand bits), as well
                 as the original IEEE double-precision values, can be
                 retrieved without duplicating the data in different
                 formats.\par

                 Our numerical experiments on an NVIDIA V100 GPU (Volta
                 architecture) and a server equipped with two Intel Xeon
                 Platinum 8168 CPUs (48 cores in total) expose that,
                 compared with a standard IEEE double-precision
                 implementation, the CPMS-based PageRank completes about
                 10\% faster if high-accuracy output is needed, and
                 about 30\% faster if reduced output accuracy is
                 acceptable.",
  acknowledgement = ack-nhfb,
  articleno =    "4",
  fjournal =     "ACM Transactions on Parallel Computing",
  journal-URL =  "https://dl.acm.org/loi/topc",
}


bibtype: article
filename: pagerank.bib
label: Guo:2020:RBE
author: Pei-Chang Guo
title: A residual-based error bound for the multilinear PageRank vector
journal: j-LIN-MULT-ALGEBRA
volume: 68
number: 3
pages: 568--574
year: 2020
CODEN: LNMLAZ
DOI: https://doi.org/10.1080/03081087.2018.1509937
ISSN: 0308-1087 (print), 1563-5139 (electronic) OR 03081087 (print), 15635139 (electronic)
ISSNL: 0308-1087 OR 03081087
bibdate: Mon Mar 9 16:30:36 MDT 2020
bibtimestamp: 2020.03.09 16:30:36 MDT
fjournal: Linear and Multilinear Algebra
acknowledgement: ack-nhfb
journalURL: http://www.tandfonline.com/loi/glma20
onlinedate: 17 Aug 2018
rawauthor: Pei-Chang Guo
rawtitle: A residual-based error bound for the multilinear {PageRank} vector
bibsource:
https://www.math.utah.edu/pub/tex/bib/linmultalgebra.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Guo:2020:RBE,
  author =       "Pei-Chang Guo",
  title =        "A residual-based error bound for the multilinear
                 {PageRank} vector",
  journal =      j-LIN-MULT-ALGEBRA,
  volume =       "68",
  number =       "3",
  pages =        "568--574",
  year =         "2020",
  CODEN =        "LNMLAZ",
  DOI =          "https://doi.org/10.1080/03081087.2018.1509937",
  ISSN =         "0308-1087 (print), 1563-5139 (electronic)",
  ISSN-L =       "0308-1087",
  bibdate =      "Mon Mar 9 16:30:36 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/linmultalgebra.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "Linear and Multilinear Algebra",
  journal-URL =  "http://www.tandfonline.com/loi/glma20",
  onlinedate =   "17 Aug 2018",
}


bibtype: article
filename: pagerank.bib
label: Li:2020:MPU
author: Wen Li and Dongdong Liu and Seak-Weng Vong and Mingqing Xiao
title: Multilinear PageRank: Uniqueness, error bound and perturbation analysis
journal: j-APPL-NUM-MATH
volume: 156
pages: 584--607
month: oct
monthnumber: 10
year: 2020
CODEN: ANMAEL
ISSN: 0168-9274 (print), 1873-5460 (electronic) OR 01689274 (print), 18735460 (electronic)
ISSNL: 0168-9274 OR 01689274
bibdate: Tue Dec 29 07:52:53 MST 2020
bibtimestamp: 2020.12.29 07:52:53 MST
URL: http://www.sciencedirect.com/science/article/pii/S0168927420301665
fjournal: Applied Numerical Mathematics: Transactions of IMACS
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/01689274
rawauthor: Wen Li and Dongdong Liu and Seak-Weng Vong and Mingqing Xiao
rawtitle: Multilinear {PageRank}: Uniqueness, error bound and perturbation analysis
bibsource:
https://www.math.utah.edu/pub/tex/bib/applnummath.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Li:2020:MPU,
  author =       "Wen Li and Dongdong Liu and Seak-Weng Vong and
                 Mingqing Xiao",
  title =        "Multilinear {PageRank}: Uniqueness, error bound and
                 perturbation analysis",
  journal =      j-APPL-NUM-MATH,
  volume =       "156",
  number =       "??",
  pages =        "584--607",
  month =        oct,
  year =         "2020",
  CODEN =        "ANMAEL",
  ISSN =         "0168-9274 (print), 1873-5460 (electronic)",
  ISSN-L =       "0168-9274",
  bibdate =      "Tue Dec 29 07:52:53 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/applnummath.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0168927420301665",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Numerical Mathematics: Transactions of IMACS",
  journal-URL =  "http://www.sciencedirect.com/science/journal/01689274",
}


bibtype: article
filename: pagerank.bib
label: Miao:2020:AAM
author: Cun-Qiang Miao and Xue-Yuan Tan
title: Accelerating the Arnoldi method via Chebyshev polynomials for computing PageRank
journal: j-J-COMPUT-APPL-MATH
volume: 377
pages: Article 112891
day: 15
month: oct
monthnumber: 10
year: 2020
CODEN: JCAMDI
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Wed May 13 06:58:35 MDT 2020
bibtimestamp: 2020.05.13 06:58:35 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0377042720301825
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Cun-Qiang Miao and Xue-Yuan Tan
rawtitle: Accelerating the {Arnoldi} method via {Chebyshev} polynomials for computing {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Miao:2020:AAM,
  author =       "Cun-Qiang Miao and Xue-Yuan Tan",
  title =        "Accelerating the {Arnoldi} method via {Chebyshev}
                 polynomials for computing {PageRank}",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "377",
  number =       "??",
  pages =        "Article 112891",
  day =          "15",
  month =        oct,
  year =         "2020",
  CODEN =        "JCAMDI",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Wed May 13 06:58:35 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042720301825",
  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: Sankpal:2020:RRA
author: Lata Jaywant Sankpal and Suhas H Patil
title: Rider-Rank Algorithm-Based Feature Extraction for Re-ranking the Webpages in the Search Engine
journal: j-COMP-J
volume: 63
number: 10
pages: 1479--1489
month: oct
monthnumber: 10
year: 2020
CODEN: CMPJA6
DOI: https://doi.org/10.1093/comjnl/bxaa032
ISSN: 0010-4620 (print), 1460-2067 (electronic) OR 00104620 (print), 14602067 (electronic)
ISSNL: 0010-4620 OR 00104620
bibdate: Mon Oct 19 08:41:03 MDT 2020
bibtimestamp: 2020.10.19 08:41:03 MDT
URL: http://academic.oup.com/comjnl/article/63/10/1479/5855737
fjournal: Computer Journal
acknowledgement: ack-nhfb
journalURL: http://comjnl.oxfordjournals.org/
rawauthor: Lata Jaywant Sankpal and Suhas H Patil
rawtitle: Rider-Rank Algorithm-Based Feature Extraction for Re-ranking the {Webpages} in the Search Engine
bibsource:
https://www.math.utah.edu/pub/tex/bib/compj2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Sankpal:2020:RRA,
  author =       "Lata Jaywant Sankpal and Suhas H Patil",
  title =        "Rider-Rank Algorithm-Based Feature Extraction for
                 Re-ranking the {Webpages} in the Search Engine",
  journal =      j-COMP-J,
  volume =       "63",
  number =       "10",
  pages =        "1479--1489",
  month =        oct,
  year =         "2020",
  CODEN =        "CMPJA6",
  DOI =          "https://doi.org/10.1093/comjnl/bxaa032",
  ISSN =         "0010-4620 (print), 1460-2067 (electronic)",
  ISSN-L =       "0010-4620",
  bibdate =      "Mon Oct 19 08:41:03 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/compj2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://academic.oup.com/comjnl/article/63/10/1479/5855737",
  acknowledgement = ack-nhfb,
  fjournal =     "Computer Journal",
  journal-URL =  "http://comjnl.oxfordjournals.org/",
}


bibtype: article
filename: pagerank.bib
label: Shi:2020:RIF
author: Jieming Shi and Tianyuan Jin and Renchi Yang and Xiaokui Xiao and Yin Yang
title: Realtime index-free single source SimRank processing on web-scale graphs
journal: j-PROC-VLDB-ENDOWMENT
volume: 13
number: 7
pages: 966--980
month: mar
monthnumber: 3
year: 2020
DOI: https://doi.org/10.14778/3384345.3384347
ISSN: 2150-8097 OR 21508097
bibdate: Tue May 5 14:01:13 MDT 2020
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbe.bib
bibtimestamp: 2020.05.05 14:01:13 MDT
URL: https://dl.acm.org/doi/abs/10.14778/3384345.3384347
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: https://dl.acm.org/loi/pvldb
rawauthor: Jieming Shi and Tianyuan Jin and Renchi Yang and Xiaokui Xiao and Yin Yang
rawtitle: Realtime index-free single source {SimRank} processing on web-scale graphs
abstract:
Given a graph $G$ and a node $ u in G$, a single source SimRank query evaluates the similarity between $u$ and every node $ v in G$. Existing approaches to single source SimRank computation incur either long query response time, or expensive pre-computation, which ldots 

rawabstract:
Given a graph $G$ and a node $ u \in G$, a single source SimRank query evaluates the similarity between $u$ and every node $ v \in G$. Existing approaches to single source SimRank computation incur either long query response time, or expensive pre-computation, which \ldots{}

entry:
@Article{Shi:2020:RIF,
  author =       "Jieming Shi and Tianyuan Jin and Renchi Yang and
                 Xiaokui Xiao and Yin Yang",
  title =        "Realtime index-free single source {SimRank} processing
                 on web-scale graphs",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "13",
  number =       "7",
  pages =        "966--980",
  month =        mar,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.14778/3384345.3384347",
  ISSN =         "2150-8097",
  bibdate =      "Tue May 5 14:01:13 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  URL =          "https://dl.acm.org/doi/abs/10.14778/3384345.3384347",
  abstract =     "Given a graph $G$ and a node $ u \in G$, a single
                 source SimRank query evaluates the similarity between
                 $u$ and every node $ v \in G$. Existing approaches to
                 single source SimRank computation incur either long
                 query response time, or expensive pre-computation,
                 which \ldots{}",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "https://dl.acm.org/loi/pvldb",
}


bibtype: article
filename: pagerank.bib
label: Xiao:2020:PRF
author: Zhijun Xiao and Cuiping Li and Hong Chen
journal: j-TWEB
volume: 14
number: 3
pages: 10:1--10:15
month: jul
monthnumber: 7
year: 2020
DOI: https://doi.org/10.1145/3386042
ISSN: 1559-1131 (print), 1559-114X (electronic) OR 15591131 (print), 1559114X (electronic)
ISSNL: 1559-1131 OR 15591131
bibdate: Wed Jul 22 17:29:55 MDT 2020
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tweb.bib
bibtimestamp: 2020.07.22 17:29:55 MDT
URL: https://dl.acm.org/doi/abs/10.1145/3386042
fjournal: ACM Transactions on the Web (TWEB)
acknowledgement: ack-nhfb
articleno: 10
journalURL: https://dl.acm.org/loi/tweb
rawauthor: Zhijun Xiao and Cuiping Li and Hong Chen
title:
PatternRank+NN: a Ranking Framework Bringing User Behaviors into Entity Set Expansion from Web Search Queries

abstract:
We propose a ranking framework, called PatternRank+NN, for expanding a set of seed entities of a particular class (i.e., entity set expansion) from Web search queries. PatternRank+NN consists of two parts: PatternRank and NN. Unlike the traditional ldots 

rawabstract:
We propose a ranking framework, called PatternRank+NN, for expanding a set of seed entities of a particular class (i.e., entity set expansion) from Web search queries. PatternRank+NN consists of two parts: PatternRank and NN. Unlike the traditional \ldots{}

rawtitle:
{PatternRank+NN}: a Ranking Framework Bringing User Behaviors into Entity Set Expansion from {Web} Search Queries

entry:
@Article{Xiao:2020:PRF,
  author =       "Zhijun Xiao and Cuiping Li and Hong Chen",
  title =        "{PatternRank+NN}: a Ranking Framework Bringing User
                 Behaviors into Entity Set Expansion from {Web} Search
                 Queries",
  journal =      j-TWEB,
  volume =       "14",
  number =       "3",
  pages =        "10:1--10:15",
  month =        jul,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3386042",
  ISSN =         "1559-1131 (print), 1559-114X (electronic)",
  ISSN-L =       "1559-1131",
  bibdate =      "Wed Jul 22 17:29:55 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tweb.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3386042",
  abstract =     "We propose a ranking framework, called PatternRank+NN,
                 for expanding a set of seed entities of a particular
                 class (i.e., entity set expansion) from Web search
                 queries. PatternRank+NN consists of two parts:
                 PatternRank and NN. Unlike the traditional \ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "10",
  fjournal =     "ACM Transactions on the Web (TWEB)",
  journal-URL =  "https://dl.acm.org/loi/tweb",
}


bibtype: article
filename: pagerank.bib
label: Yang:2020:HNE
author: Renchi Yang and Jieming Shi and Xiaokui Xiao and Yin Yang and Sourav S. Bhowmick
title: Homogeneous network embedding for massive graphs via reweighted personalized PageRank
journal: j-PROC-VLDB-ENDOWMENT
volume: 13
number: 5
pages: 670--683
month: jan
monthnumber: 1
year: 2020
DOI: https://doi.org/10.14778/3377369.3377376
ISSN: 2150-8097 OR 21508097
bibdate: Thu Apr 2 10:51:27 MDT 2020
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbe.bib
bibtimestamp: 2020.04.02 10:51:27 MDT
URL: https://dl.acm.org/doi/abs/10.14778/3377369.3377376
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: https://dl.acm.org/loi/pvldb
rawauthor: Renchi Yang and Jieming Shi and Xiaokui Xiao and Yin Yang and Sourav S. Bhowmick
rawtitle: Homogeneous network embedding for massive graphs via reweighted personalized {PageRank}
abstract:
Given an input graph G and a node $ v in G $, homogeneous network embedding (HNE) maps the graph structure in the vicinity of $v$ to a compact, fixed-dimensional feature vector. This paper focuses on HNE for massive graphs, e.g., with billions of edges. On ldots 

rawabstract:
Given an input graph G and a node $ v \in G $, homogeneous network embedding (HNE) maps the graph structure in the vicinity of $v$ to a compact, fixed-dimensional feature vector. This paper focuses on HNE for massive graphs, e.g., with billions of edges. On \ldots{}

entry:
@Article{Yang:2020:HNE,
  author =       "Renchi Yang and Jieming Shi and Xiaokui Xiao and Yin
                 Yang and Sourav S. Bhowmick",
  title =        "Homogeneous network embedding for massive graphs via
                 reweighted personalized {PageRank}",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "13",
  number =       "5",
  pages =        "670--683",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.14778/3377369.3377376",
  ISSN =         "2150-8097",
  bibdate =      "Thu Apr 2 10:51:27 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  URL =          "https://dl.acm.org/doi/abs/10.14778/3377369.3377376",
  abstract =     "Given an input graph G and a node $ v \in G $,
                 homogeneous network embedding (HNE) maps the graph
                 structure in the vicinity of $v$ to a compact,
                 fixed-dimensional feature vector. This paper focuses on
                 HNE for massive graphs, e.g., with billions of edges.
                 On \ldots{}",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "https://dl.acm.org/loi/pvldb",
}


bibtype: article
filename: pagerank.bib
label: Abadeh:2021:DED
author: Maryam Nooraei Abadeh and Mansooreh Mirzaie
title: DiffPageRank: an efficient differential PageRank approach in MapReduce
journal: j-J-SUPERCOMPUTING
volume: 77
number: 1
pages: 188--211
month: jan
monthnumber: 1
year: 2021
CODEN: JOSUED
DOI: https://doi.org/10.1007/s11227-020-03265-3
ISSN: 0920-8542 (print), 1573-0484 (electronic) OR 09208542 (print), 15730484 (electronic)
ISSNL: 0920-8542 OR 09208542
bibdate: Fri May 14 09:19:58 MDT 2021
bibtimestamp: 2021.05.14 09:19:58 MDT
URL: https://link.springer.com/article/10.1007/s11227-020-03265-3
fjournal: The Journal of Supercomputing
acknowledgement: ack-nhfb
journalURL: http://link.springer.com/journal/11227
rawauthor: Maryam Nooraei Abadeh and Mansooreh Mirzaie
rawtitle: {DiffPageRank}: an efficient differential {PageRank} approach in {MapReduce}
bibsource:
https://www.math.utah.edu/pub/tex/bib/jsuper.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Abadeh:2021:DED,
  author =       "Maryam Nooraei Abadeh and Mansooreh Mirzaie",
  title =        "{DiffPageRank}: an efficient differential {PageRank}
                 approach in {MapReduce}",
  journal =      j-J-SUPERCOMPUTING,
  volume =       "77",
  number =       "1",
  pages =        "188--211",
  month =        jan,
  year =         "2021",
  CODEN =        "JOSUED",
  DOI =          "https://doi.org/10.1007/s11227-020-03265-3",
  ISSN =         "0920-8542 (print), 1573-0484 (electronic)",
  ISSN-L =       "0920-8542",
  bibdate =      "Fri May 14 09:19:58 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jsuper.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s11227-020-03265-3",
  acknowledgement = ack-nhfb,
  fjournal =     "The Journal of Supercomputing",
  journal-URL =  "http://link.springer.com/journal/11227",
  online-date =  "Published: 30 March 2020 Pages: 188 - 211",
}


bibtype: article
filename: pagerank.bib
label: Amodio:2021:IPA
author: Pierluigi Amodio and Luigi Brugnano and Filippo Scarselli
title: Implementation of the PaperRank and AuthorRank indices in the Scopus database
journal: j-J-INFORMETRICS
volume: 15
number: 4
pages: Article 101206
month: nov
monthnumber: 11
year: 2021
DOI: https://doi.org/10.1016/j.joi.2021.101206
ISSN: 1751-1577 (print), 1875-5879 (electronic) OR 17511577 (print), 18755879 (electronic)
ISSNL: 1751-1577 OR 17511577
bibdate: Thu Mar 10 06:27:37 MST 2022
bibtimestamp: 2022.03.10 06:27:37 MST
URL: http://www.sciencedirect.com/science/article/pii/S1751157721000778
fjournal: Journal of Informetrics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/17511577/
rawauthor: Pierluigi Amodio and Luigi Brugnano and Filippo Scarselli
rawtitle: Implementation of the {PaperRank} and {AuthorRank} indices in the {Scopus} database
entry:
@Article{Amodio:2021:IPA,
  author =       "Pierluigi Amodio and Luigi Brugnano and Filippo
                 Scarselli",
  title =        "Implementation of the {PaperRank} and {AuthorRank}
                 indices in the {Scopus} database",
  journal =      j-J-INFORMETRICS,
  volume =       "15",
  number =       "4",
  pages =        "Article 101206",
  month =        nov,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1016/j.joi.2021.101206",
  ISSN =         "1751-1577 (print), 1875-5879 (electronic)",
  ISSN-L =       "1751-1577",
  bibdate =      "Thu Mar 10 06:27:37 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jinformetrics.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1751157721000778",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Informetrics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/17511577/",
}


bibtype: article
filename: pagerank.bib
label: Hou:2021:MPA
author: Guanhao Hou and Xingguang Chen and Sibo Wang and Zhewei Wei
title: Massively parallel algorithms for Personalized PageRank
journal: j-PROC-VLDB-ENDOWMENT
volume: 14
number: 9
pages: 1668--1680
month: may
monthnumber: 5
year: 2021
DOI: https://doi.org/10.14778/3461535.3461554
ISSN: 2150-8097 OR 21508097
bibdate: Sat Oct 23 06:39:32 MDT 2021
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbe.bib
bibtimestamp: 2021.10.23 06:39:32 MDT
URL: https://dl.acm.org/doi/10.14778/3461535.3461554
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: https://dl.acm.org/loi/pvldb
rawauthor: Guanhao Hou and Xingguang Chen and Sibo Wang and Zhewei Wei
rawtitle: Massively parallel algorithms for {Personalized PageRank}
abstract:
Personalized PageRank (PPR) has wide applications in search engines, social recommendations, community detection, and so on. Nowadays, graphs are becoming massive and many IT companies need to deal with large graphs that cannot be fitted into the memory of most commodity servers. However, most existing state-of-the-art solutions for PPR computation only work for single-machines and are inefficient for the distributed framework since such solutions either (i) result in an excessively large number of communication rounds, or (ii) incur high communication costs in each round. Motivated by this, we present Delta-Push, an efficient framework for single-source and top-$k$ PPR queries in distributed settings. Our goal is to reduce the number of rounds while guaranteeing that the load, i.e., the maximum number of messages an executor sends or receives in a round, can be bounded by the capacity of each executor. We first present a non-trivial combination of a redesigned parallel push algorithm and the Monte-Carlo method to answer single-source PPR queries. The solution uses pre-sampled random walks to reduce the number of rounds for the push algorithm. Theoretical analysis under the Massively Parallel Computing (MPC) model shows that our proposed solution bounds the communication rounds to [EQUATION] under a load of O(m/p), where m is the number of edges of the input graph, p is the number of executors, and $ epsilon $ is a user-defined error parameter. In the meantime, as the number of executors increases to $ p' = gamma cdot p$, the load constraint can be relaxed since each executor can hold $ O(gamma cdot m / p')$ messages with invariant local memory. In such scenarios, multiple queries can be processed in batches simultaneously. We show that with a load of $ O(gamma cdot m / p')$, our Delta-Push can process $ gamma $ queries in a batch with [EQUATION] rounds, while other baseline solutions still keep the same round cost for each batch. We further present a new top-$k$ algorithm that is friendly to the distributed framework and reduces the number of rounds required in practice. Extensive experiments show that our proposed solution is more efficient than alternatives.

rawabstract:
Personalized PageRank (PPR) has wide applications in search engines, social recommendations, community detection, and so on. Nowadays, graphs are becoming massive and many IT companies need to deal with large graphs that cannot be fitted into the memory of most commodity servers. However, most existing state-of-the-art solutions for PPR computation only work for single-machines and are inefficient for the distributed framework since such solutions either (i) result in an excessively large number of communication rounds, or (ii) incur high communication costs in each round. Motivated by this, we present Delta-Push, an efficient framework for single-source and top-$k$ PPR queries in distributed settings. Our goal is to reduce the number of rounds while guaranteeing that the load, i.e., the maximum number of messages an executor sends or receives in a round, can be bounded by the capacity of each executor. We first present a non-trivial combination of a redesigned parallel push algorithm and the Monte-Carlo method to answer single-source PPR queries. The solution uses pre-sampled random walks to reduce the number of rounds for the push algorithm. Theoretical analysis under the Massively Parallel Computing (MPC) model shows that our proposed solution bounds the communication rounds to [EQUATION] under a load of O(m/p), where m is the number of edges of the input graph, p is the number of executors, and $ \epsilon $ is a user-defined error parameter. In the meantime, as the number of executors increases to $ p' = \gamma \cdot p$, the load constraint can be relaxed since each executor can hold $ O(\gamma \cdot m / p')$ messages with invariant local memory. In such scenarios, multiple queries can be processed in batches simultaneously. We show that with a load of $ O(\gamma \cdot m / p')$, our Delta-Push can process $ \gamma $ queries in a batch with [EQUATION] rounds, while other baseline solutions still keep the same round cost for each batch. We further present a new top-$k$ algorithm that is friendly to the distributed framework and reduces the number of rounds required in practice. Extensive experiments show that our proposed solution is more efficient than alternatives.

entry:
@Article{Hou:2021:MPA,
  author =       "Guanhao Hou and Xingguang Chen and Sibo Wang and
                 Zhewei Wei",
  title =        "Massively parallel algorithms for {Personalized
                 PageRank}",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "14",
  number =       "9",
  pages =        "1668--1680",
  month =        may,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.14778/3461535.3461554",
  ISSN =         "2150-8097",
  bibdate =      "Sat Oct 23 06:39:32 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  URL =          "https://dl.acm.org/doi/10.14778/3461535.3461554",
  abstract =     "Personalized PageRank (PPR) has wide applications in
                 search engines, social recommendations, community
                 detection, and so on. Nowadays, graphs are becoming
                 massive and many IT companies need to deal with large
                 graphs that cannot be fitted into the memory of most
                 commodity servers. However, most existing
                 state-of-the-art solutions for PPR computation only
                 work for single-machines and are inefficient for the
                 distributed framework since such solutions either (i)
                 result in an excessively large number of communication
                 rounds, or (ii) incur high communication costs in each
                 round.

                 Motivated by this, we present Delta-Push, an efficient
                 framework for single-source and top-$k$ PPR queries in
                 distributed settings. Our goal is to reduce the number
                 of rounds while guaranteeing that the load, i.e., the
                 maximum number of messages an executor sends or
                 receives in a round, can be bounded by the capacity of
                 each executor. We first present a non-trivial
                 combination of a redesigned parallel push algorithm and
                 the Monte-Carlo method to answer single-source PPR
                 queries. The solution uses pre-sampled random walks to
                 reduce the number of rounds for the push algorithm.
                 Theoretical analysis under the Massively Parallel
                 Computing (MPC) model shows that our proposed solution
                 bounds the communication rounds to [EQUATION] under a
                 load of O(m/p), where m is the number of edges of the
                 input graph, p is the number of executors, and $
                 \epsilon $ is a user-defined error parameter. In the
                 meantime, as the number of executors increases to $ p'
                 = \gamma \cdot p$, the load constraint can be relaxed
                 since each executor can hold $ O(\gamma \cdot m / p')$
                 messages with invariant local memory. In such
                 scenarios, multiple queries can be processed in batches
                 simultaneously. We show that with a load of $ O(\gamma
                 \cdot m / p')$, our Delta-Push can process $ \gamma $
                 queries in a batch with [EQUATION] rounds, while other
                 baseline solutions still keep the same round cost for
                 each batch. We further present a new top-$k$ algorithm
                 that is friendly to the distributed framework and
                 reduces the number of rounds required in practice.
                 Extensive experiments show that our proposed solution
                 is more efficient than alternatives.",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "https://dl.acm.org/loi/pvldb",
}


bibtype: article
filename: pagerank.bib
label: Hu:2021:VPA
author: Qian-Ying Hu and Chun Wen and Ting-Zhu Huang and Zhao-Li Shen and Xian-Ming Gu
title: A variant of the Power--Arnoldi algorithm for computing PageRank
journal: j-J-COMPUT-APPL-MATH
volume: 381
pages: Article 113034
day: 1
month: jan
monthnumber: 1
year: 2021
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2020.113034
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Sat Mar 27 09:45:44 MDT 2021
bibtimestamp: 2021.03.27 09:45:44 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0377042720303253
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Qian-Ying Hu and Chun Wen and Ting-Zhu Huang and Zhao-Li Shen and Xian-Ming Gu
rawtitle: A variant of the {Power--Arnoldi} algorithm for computing {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Hu:2021:VPA,
  author =       "Qian-Ying Hu and Chun Wen and Ting-Zhu Huang and
                 Zhao-Li Shen and Xian-Ming Gu",
  title =        "A variant of the {Power--Arnoldi} algorithm for
                 computing {PageRank}",
  journal =      j-J-COMPUT-APPL-MATH,
  volume =       "381",
  number =       "??",
  pages =        "Article 113034",
  day =          "1",
  month =        jan,
  year =         "2021",
  CODEN =        "JCAMDI",
  DOI =          "https://doi.org/10.1016/j.cam.2020.113034",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Sat Mar 27 09:45:44 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042720303253",
  acknowledgement = ack-nhfb,
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
}


bibtype: article
filename: pagerank.bib
label: Huang:2021:CFP
author: Jun Huang and Gang Wu
title: Convergence of the fixed-point iteration for multilinear PageRank
journal: j-NUM-LIN-ALG-APPL
volume: 28
number: 5
pages: e2379:1--e2379:??
month: oct
monthnumber: 10
year: 2021
CODEN: NLAAEM
DOI: https://doi.org/10.1002/nla.2379
ISSN: 1070-5325 (print), 1099-1506 (electronic) OR 10705325 (print), 10991506 (electronic)
ISSNL: 1070-5325 OR 10705325
bibdate: Mon Feb 21 13:12:20 MST 2022
bibtimestamp: 2022.02.21 13:12:20 MST
fjournal: Numerical Linear Algebra with Applications
acknowledgement: ack-nhfb
ajournal: Num. Lin. Alg. Appl.
journalURL: https://onlinelibrary.wiley.com/journal/10991506
onlinedate: 25 March 2021
rawauthor: Jun Huang and Gang Wu
rawtitle: Convergence of the fixed-point iteration for multilinear {PageRank}
entry:
@Article{Huang:2021:CFP,
  author =       "Jun Huang and Gang Wu",
  title =        "Convergence of the fixed-point iteration for
                 multilinear {PageRank}",
  journal =      j-NUM-LIN-ALG-APPL,
  volume =       "28",
  number =       "5",
  pages =        "e2379:1--e2379:??",
  month =        oct,
  year =         "2021",
  CODEN =        "NLAAEM",
  DOI =          "https://doi.org/10.1002/nla.2379",
  ISSN =         "1070-5325 (print), 1099-1506 (electronic)",
  ISSN-L =       "1070-5325",
  bibdate =      "Mon Feb 21 13:12:20 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "Num. Lin. Alg. Appl.",
  fjournal =     "Numerical Linear Algebra with Applications",
  journal-URL =  "https://onlinelibrary.wiley.com/journal/10991506",
  onlinedate =   "25 March 2021",
}


bibtype: article
filename: pagerank.bib
label: Olvera-Cravioto:2021:PBU
author: Mariana Olvera-Cravioto
title: PageRank's behavior under degree correlations
journal: j-ANN-APPL-PROBAB
volume: 31
number: 3
pages: 1403--1442
month: jun
monthnumber: 6
year: 2021
DOI: https://doi.org/10.1214/20-AAP1623
ISSN: 1050-5164 (print), 2168-8737 (electronic) OR 10505164 (print), 21688737 (electronic)
ISSNL: 1050-5164 OR 10505164
MRclass: 05C80; 60J80; 41A60; 60B10
bibdate: Wed Apr 6 07:46:07 MDT 2022
bibtimestamp: 2022.04.06 07:46:07 MDT
fjournal: Annals of Applied Probability
acknowledgement: ack-nhfb
ajournal: Ann. Appl. Probab.
journalURL: http://projecteuclid.org/all/euclid.aoap/; http://www.jstor.org/journals/10505164.html
rawauthor: Mariana Olvera-Cravioto
rawtitle: {PageRank's} behavior under degree correlations
bibsource:
https://www.math.utah.edu/pub/tex/bib/annapplprobab.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

URL:
https://projecteuclid.org/journals/annals-of-applied-probability/volume-31/issue-3/PageRanks-behavior-under-degree-correlations/10.1214/20-AAP1623.full

keywords:
complex networks; degree-correlations; Directed random graphs; distributional fixed-point equations; PageRank; power laws; ranking algorithms; Weighted branching processes

entry:
@Article{Olvera-Cravioto:2021:PBU,
  author =       "Mariana Olvera-Cravioto",
  title =        "{PageRank's} behavior under degree correlations",
  journal =      j-ANN-APPL-PROBAB,
  volume =       "31",
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  bibdate =      "Wed Apr 6 07:46:07 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/annapplprobab.bib;
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  URL =          "https://projecteuclid.org/journals/annals-of-applied-probability/volume-31/issue-3/PageRanks-behavior-under-degree-correlations/10.1214/20-AAP1623.full",
  acknowledgement = ack-nhfb,
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bibtype: inproceedings
filename: pagerank.bib
label: Pelletier:2021:GJP
author: Michel Pelletier and Will Kimmerer and Timothy A. Davis and Timothy G. Mattson
editor: IEEE
booktitle: 2021 IEEE High Performance Extreme Computing Conference (HPEC)
title: The GraphBLAS in Julia and Python: the PageRank and Triangle Centralities
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 1--7
year: 2021
DOI: https://doi.org/10.1109/HPEC49654.2021.9622789
bibdate: Mon Dec 18 08:06:55 2023
bibtimestamp: 2023.12.18 08:06:55 ???
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bibtype: article
filename: pagerank.bib
label: Tian:2021:SRI
author: Zhaolu Tian and Yan Zhang and Junxin Wang and Chuanqing Gu
title: Several relaxed iteration methods for computing PageRank
journal: j-J-COMPUT-APPL-MATH
volume: 388
pages: Article 113295
day: 1
month: may
monthnumber: 5
year: 2021
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DOI: https://doi.org/10.1016/j.cam.2020.113295
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bibdate: Sat Mar 27 09:45:47 MDT 2021
bibtimestamp: 2021.03.27 09:45:47 MDT
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rawauthor: Zhaolu Tian and Yan Zhang and Junxin Wang and Chuanqing Gu
rawtitle: Several relaxed iteration methods for computing {PageRank}
bibsource:
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bibtype: article
filename: pagerank.bib
label: Tortosa:2021:ARN
author: Leandro Tortosa and Jose F. Vicent and Gevorg Yeghikyan
title: An algorithm for ranking the nodes of multiplex networks with data based on the PageRank concept
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volume: 392
pages: Article 125676
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month: mar
monthnumber: 3
year: 2021
CODEN: AMHCBQ
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ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
bibdate: Sat Mar 13 06:39:51 MST 2021
bibtimestamp: 2021.03.13 06:39:51 MST
URL: http://www.sciencedirect.com/science/article/pii/S0096300320306299
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acknowledgement: ack-nhfb
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bibtype: article
filename: pagerank.bib
label: Wen:2021:APG
author: Chun Wen and Qian-Ying Hu and Guo-Jian Yin and Xian-Ming Gu and Zhao-Li Shen
title: An adaptive Power--Arnoldi algorithm for computing PageRank
journal: j-J-COMPUT-APPL-MATH
volume: 386
pages: Article 113209
month: apr
monthnumber: 4
year: 2021
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2020.113209
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
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bibdate: Sat Mar 27 09:45:47 MDT 2021
bibtimestamp: 2021.03.27 09:45:47 MDT
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rawauthor: Chun Wen and Qian-Ying Hu and Guo-Jian Yin and Xian-Ming Gu and Zhao-Li Shen
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bibtype: article
filename: pagerank.bib
label: Zhang:2021:ESB
title: An Empirical Study of Boosting Spectrum-Based Fault Localization via PageRank
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volume: 47
number: 6
pages: 1089--1113
month: jun
monthnumber: 6
year: 2021
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ISSN: 0098-5589 (print), 1939-3520 (electronic) OR 00985589 (print), 19393520 (electronic)
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bibdate: Thu Jun 17 08:11:01 2021
bibtimestamp: 2021.06.17 08:11:01 ???
fjournal: IEEE Transactions on Software Engineering
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rawtitle: An Empirical Study of Boosting Spectrum-Based Fault Localization via {PageRank}
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bibtype: article
filename: pagerank.bib
label: Banerjee:2022:PAD
author: Sayan Banerjee and Mariana Olvera-Cravioto
title: PageRank asymptotics on directed preferential attachment networks
journal: j-ANN-APPL-PROBAB
volume: 32
number: 4
pages: 3060--3084
month: aug
monthnumber: 8
year: 2022
DOI: https://doi.org/10.1214/21-AAP1757
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MRclass: 05C80; 60J80; 68P10; 41A60; 60B10
bibdate: Wed Mar 22 16:13:27 MDT 2023
bibtimestamp: 2023.03.22 16:13:27 MDT
fjournal: Annals of Applied Probability
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journalURL: http://projecteuclid.org/all/euclid.aoap/; http://www.jstor.org/journals/10505164.html
rawauthor: Sayan Banerjee and Mariana Olvera-Cravioto
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keywords:
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bibtype: article
filename: pagerank.bib
label: Bucci:2022:CMC
author: Alberto Bucci and Federico Poloni
title: A continuation method for computing the multilinear PageRank
journal: j-NUM-LIN-ALG-APPL
volume: 29
number: 4
pages: e2432:1--e2432:??
month: aug
monthnumber: 8
year: 2022
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bibdate: Fri Mar 3 12:16:00 MST 2023
bibtimestamp: 2023.03.03 12:16:00 MST
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bibtype: article
filename: pagerank.bib
label: Eedi:2022:IOP
author: Hemalatha Eedi and Sahith Karra and Rahul Utkoor
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number: 3-4
pages: 381--404
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monthnumber: 8
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bibdate: Fri Jul 15 17:25:07 MDT 2022
bibtimestamp: 2022.07.15 17:25:07 MDT
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bibtype: article
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label: Gu:2022:HTA
author: Xian-Ming Gu and Siu-Long Lei and Bruno Carpentieri
title: A Hessenberg-type algorithm for computing PageRank Problems
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volume: 89
number: 4
pages: 1845--1863
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monthnumber: 4
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bibtype: article
filename: pagerank.bib
label: Jin:2022:SGA
author: Yu Jin and Chun Wen and Zhao-Li Shen and Xian-Ming Gu
title: A simpler GMRES algorithm accelerated by Chebyshev polynomials for computing PageRank
journal: j-J-COMPUT-APPL-MATH
volume: 413
pages: ??--??
day: 15
month: oct
monthnumber: 10
year: 2022
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bibdate: Fri May 27 15:22:59 MDT 2022
bibtimestamp: 2022.05.27 15:22:59 MDT
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  year =         "2022",
  CODEN =        "JCAMDI",
  DOI =          "https://doi.org/10.1016/j.cam.2022.114395",
  ISSN =         "0377-0427 (print), 1879-1778 (electronic)",
  ISSN-L =       "0377-0427",
  bibdate =      "Fri May 27 15:22:59 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jcomputapplmath2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0377042722001819",
  acknowledgement = ack-nhfb,
  articleno =    "114395",
  fjournal =     "Journal of Computational and Applied Mathematics",
  journal-URL =  "http://www.sciencedirect.com/science/journal/03770427",
}


bibtype: article
filename: pagerank.bib
label: Shen:2022:SPG
author: Zhao-Li Shen and Meng Su and Bruno Carpentieri and Chun Wen
journal: j-APPL-MATH-COMP
volume: 420
pages: Article 126799
day: 1
month: may
monthnumber: 5
year: 2022
CODEN: AMHCBQ
DOI: https://doi.org/10.1016/j.amc.2021.126799
ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
bibdate: Mon Jan 31 07:59:07 MST 2022
bibtimestamp: 2022.01.31 07:59:07 MST
URL: http://www.sciencedirect.com/science/article/pii/S009630032100881X
fjournal: Applied Mathematics and Computation
acknowledgement: ack-nhfb
journalURL: http://www.sciencedirect.com/science/journal/00963003
rawauthor: Zhao-Li Shen and Meng Su and Bruno Carpentieri and Chun Wen
title:
Shifted power-GMRES method accelerated by extrapolation for solving PageRank with multiple damping factors

bibsource:
https://www.math.utah.edu/pub/tex/bib/applmathcomput2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

rawtitle:
Shifted power-{GMRES} method accelerated by extrapolation for solving {PageRank} with multiple damping factors

entry:
@Article{Shen:2022:SPG,
  author =       "Zhao-Li Shen and Meng Su and Bruno Carpentieri and
                 Chun Wen",
  title =        "Shifted power-{GMRES} method accelerated by
                 extrapolation for solving {PageRank} with multiple
                 damping factors",
  journal =      j-APPL-MATH-COMP,
  volume =       "420",
  number =       "??",
  pages =        "Article 126799",
  day =          "1",
  month =        may,
  year =         "2022",
  CODEN =        "AMHCBQ",
  DOI =          "https://doi.org/10.1016/j.amc.2021.126799",
  ISSN =         "0096-3003 (print), 1873-5649 (electronic)",
  ISSN-L =       "0096-3003",
  bibdate =      "Mon Jan 31 07:59:07 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/applmathcomput2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S009630032100881X",
  acknowledgement = ack-nhfb,
  fjournal =     "Applied Mathematics and Computation",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00963003",
}


bibtype: article
filename: pagerank.bib
label: Tian:2022:CIA
author: Zhaolu Tian and Zhongyun Liu and Yinghui Dong
title: The coupled iteration algorithms for computing PageRank
journal: j-NUMER-ALGORITHMS
volume: 89
number: 4
pages: 1603--1637
month: apr
monthnumber: 4
year: 2022
CODEN: NUALEG
DOI: https://doi.org/10.1007/s11075-021-01166-x
ISSN: 1017-1398 (print), 1572-9265 (electronic) OR 10171398 (print), 15729265 (electronic)
ISSNL: 1017-1398 OR 10171398
bibdate: Wed Mar 23 06:29:40 MDT 2022
bibtimestamp: 2022.03.23 06:29:40 MDT
URL: https://link.springer.com/article/10.1007/s11075-021-01166-x
fjournal: Numerical Algorithms
acknowledgement: ack-nhfb
ajournal: Numer. Algorithms
journalURL: http://link.springer.com/journal/11075
rawauthor: Zhaolu Tian and Zhongyun Liu and Yinghui Dong
rawtitle: The coupled iteration algorithms for computing {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/numeralgorithms.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Tian:2022:CIA,
  author =       "Zhaolu Tian and Zhongyun Liu and Yinghui Dong",
  title =        "The coupled iteration algorithms for computing
                 {PageRank}",
  journal =      j-NUMER-ALGORITHMS,
  volume =       "89",
  number =       "4",
  pages =        "1603--1637",
  month =        apr,
  year =         "2022",
  CODEN =        "NUALEG",
  DOI =          "https://doi.org/10.1007/s11075-021-01166-x",
  ISSN =         "1017-1398 (print), 1572-9265 (electronic)",
  ISSN-L =       "1017-1398",
  bibdate =      "Wed Mar 23 06:29:40 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/numeralgorithms.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s11075-021-01166-x",
  acknowledgement = ack-nhfb,
  ajournal =     "Numer. Algorithms",
  fjournal =     "Numerical Algorithms",
  journal-URL =  "http://link.springer.com/journal/11075",
}


bibtype: article
filename: pagerank.bib
label: Wang:2022:EBL
author: Hanzhi Wang and Zhewei Wei and Junhao Gan and Ye Yuan and Xiaoyong Du and Ji-Rong Wen
title: Edge-based local push for personalized PageRank
journal: j-PROC-VLDB-ENDOWMENT
volume: 15
number: 7
pages: 1376--1389
month: mar
monthnumber: 3
year: 2022
DOI: https://doi.org/10.14778/3523210.3523216
ISSN: 2150-8097 OR 21508097
bibdate: Fri Jun 24 09:22:18 MDT 2022
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbe.bib
bibtimestamp: 2022.06.24 09:22:18 MDT
URL: https://dl.acm.org/doi/10.14778/3523210.3523216
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: https://dl.acm.org/loi/pvldb
rawauthor: Hanzhi Wang and Zhewei Wei and Junhao Gan and Ye Yuan and Xiaoyong Du and Ji-Rong Wen
rawtitle: Edge-based local push for personalized {PageRank}
abstract:
Personalized PageRank (PPR) is a popular node proximity metric in graph mining and network research. A single-source PPR (SSPPR) query asks for the PPR value of each node on the graph. Due to its importance and wide applications, decades of efforts have ldots 

rawabstract:
Personalized PageRank (PPR) is a popular node proximity metric in graph mining and network research. A single-source PPR (SSPPR) query asks for the PPR value of each node on the graph. Due to its importance and wide applications, decades of efforts have \ldots{}

entry:
@Article{Wang:2022:EBL,
  author =       "Hanzhi Wang and Zhewei Wei and Junhao Gan and Ye Yuan
                 and Xiaoyong Du and Ji-Rong Wen",
  title =        "Edge-based local push for personalized {PageRank}",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "15",
  number =       "7",
  pages =        "1376--1389",
  month =        mar,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.14778/3523210.3523216",
  ISSN =         "2150-8097",
  bibdate =      "Fri Jun 24 09:22:18 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  URL =          "https://dl.acm.org/doi/10.14778/3523210.3523216",
  abstract =     "Personalized PageRank (PPR) is a popular node
                 proximity metric in graph mining and network research.
                 A single-source PPR (SSPPR) query asks for the PPR
                 value of each node on the graph. Due to its importance
                 and wide applications, decades of efforts have
                 \ldots{}",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "https://dl.acm.org/loi/pvldb",
}


bibtype: article
filename: pagerank.bib
label: Zhong:2022:SBC
author: Han Zhong and Zheng Li and Peng Chen and Hao Lu and Yijia Xu
title: The selection of burglary cases based on multidimensional features and PageRank
journal: j-CCPE
volume: 34
number: 10
pages: e6723:1--e6723:??
day: 1
month: may
monthnumber: 5
year: 2022
CODEN: CCPEBO
DOI: https://doi.org/10.1002/cpe.6723
ISSN: 1532-0626 (print), 1532-0634 (electronic) OR 15320626 (print), 15320634 (electronic)
ISSNL: 1532-0626 OR 15320626
bibdate: Wed Apr 13 09:55:03 MDT 2022
bibsource: https://www.math.utah.edu/pub/tex/bib/ccpe.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib
bibtimestamp: 2022.04.13 09:55:03 MDT
fjournal: Concurrency and Computation: Practice and Experience
acknowledgement: ack-nhfb
ajournal: Concurr. Comput.
journalURL: http://www.interscience.wiley.com/jpages/1532-0626
onlinedate: 30 November 2021
rawauthor: Han Zhong and Zheng Li and Peng Chen and Hao Lu and Yijia Xu
rawtitle: The selection of burglary cases based on multidimensional features and {PageRank}
entry:
@Article{Zhong:2022:SBC,
  author =       "Han Zhong and Zheng Li and Peng Chen and Hao Lu and
                 Yijia Xu",
  title =        "The selection of burglary cases based on
                 multidimensional features and {PageRank}",
  journal =      j-CCPE,
  volume =       "34",
  number =       "10",
  pages =        "e6723:1--e6723:??",
  day =          "1",
  month =        may,
  year =         "2022",
  CODEN =        "CCPEBO",
  DOI =          "https://doi.org/10.1002/cpe.6723",
  ISSN =         "1532-0626 (print), 1532-0634 (electronic)",
  ISSN-L =       "1532-0626",
  bibdate =      "Wed Apr 13 09:55:03 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ccpe.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "Concurr. Comput.",
  fjournal =     "Concurrency and Computation: Practice and Experience",
  journal-URL =  "http://www.interscience.wiley.com/jpages/1532-0626",
  onlinedate =   "30 November 2021",
}


bibtype: article
filename: pagerank.bib
label: Bowater:2023:EAP
author: David Bowater and Emmanuel Stefanakis
journal: j-APPL-MATH-COMP
volume: 446
pages: ??--??
day: 1
month: jun
monthnumber: 6
year: 2023
CODEN: AMHCBQ
DOI: https://doi.org/10.1016/j.amc.2023.127888
ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
bibdate: Thu Feb 23 11:23:36 MST 2023
bibtimestamp: 2023.02.23 11:23:36 MST
URL: http://www.sciencedirect.com/science/article/pii/S0096300323000577
fjournal: Applied Mathematics and Computation
acknowledgement: ack-nhfb
articleno: 127888
journalURL: http://www.sciencedirect.com/science/journal/00963003
rawauthor: David Bowater and Emmanuel Stefanakis
title:
Extending the Adapted PageRank Algorithm centrality model for urban street networks using non-local random walks

bibsource:
https://www.math.utah.edu/pub/tex/bib/applmathcomput2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

rawtitle:
Extending the {Adapted PageRank Algorithm} centrality model for urban street networks using non-local random walks

entry:
@Article{Bowater:2023:EAP,
  author =       "David Bowater and Emmanuel Stefanakis",
  title =        "Extending the {Adapted PageRank Algorithm} centrality
                 model for urban street networks using non-local random
                 walks",
  journal =      j-APPL-MATH-COMP,
  volume =       "446",
  number =       "??",
  pages =        "??--??",
  day =          "1",
  month =        jun,
  year =         "2023",
  CODEN =        "AMHCBQ",
  DOI =          "https://doi.org/10.1016/j.amc.2023.127888",
  ISSN =         "0096-3003 (print), 1873-5649 (electronic)",
  ISSN-L =       "0096-3003",
  bibdate =      "Thu Feb 23 11:23:36 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/applmathcomput2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0096300323000577",
  acknowledgement = ack-nhfb,
  articleno =    "127888",
  fjournal =     "Applied Mathematics and Computation",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00963003",
}


bibtype: article
filename: pagerank.bib
label: Carchiolo:2023:ENP
title: Efficient Node PageRank Improvement via Link-building using Geometric Deep Learning
journal: j-TKDD
volume: 17
number: 3
pages: 38:1--38:??
month: apr
monthnumber: 4
year: 2023
DOI: https://doi.org/10.1145/3551642
ISSN: 1556-4681 (print), 1556-472X (electronic) OR 15564681 (print), 1556472X (electronic)
ISSNL: 1556-4681 OR 15564681
bibdate: Fri Mar 31 09:53:45 MDT 2023
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tkdd.bib
bibtimestamp: 2023.03.31 09:53:45 MDT
URL: https://dl.acm.org/doi/10.1145/3551642
fjournal: ACM Transactions on Knowledge Discovery from Data (TKDD)
acknowledgement: ack-nhfb
articleno: 38
journalURL: https://dl.acm.org/loi/tkdd
rawtitle: Efficient Node {PageRank} Improvement via Link-building using Geometric Deep Learning
author:
Vincenza Carchiolo and Marco Grassia and Alessandro Longheu and Michele Malgeri and Giuseppe Mangioni

abstract:
Centrality is a relevant topic in the field of network research, due to its various theoretical and practical implications. In general, all centrality metrics aim at measuring the importance of nodes (according to some definition of importance), and such importance scores are used to rank the nodes in the network, therefore the rank improvement is a strictly related topic. In a given network, the rank improvement is achieved by establishing new links, therefore the question shifts to which and how many links should be collected to get a desired rank. This problem, also known as link-building has been shown to be NP-hard, and most heuristics developed failed in obtaining good performance with acceptable computational complexity. In this article, we present LB--GDM, a novel approach that leverages Geometric Deep Learning to tackle the link-building problem. To validate our proposal, 31 real-world networks were considered; tests show that LB--GDM performs significantly better than the state-of-the-art heuristics, while having a comparable or even lower computational complexity, which allows it to scale well even to large networks.ldots 

rawabstract:
Centrality is a relevant topic in the field of network research, due to its various theoretical and practical implications. In general, all centrality metrics aim at measuring the importance of nodes (according to some definition of importance), and such importance scores are used to rank the nodes in the network, therefore the rank improvement is a strictly related topic. In a given network, the rank improvement is achieved by establishing new links, therefore the question shifts to which and how many links should be collected to get a desired rank. This problem, also known as link-building has been shown to be NP-hard, and most heuristics developed failed in obtaining good performance with acceptable computational complexity. In this article, we present LB--GDM, a novel approach that leverages Geometric Deep Learning to tackle the link-building problem. To validate our proposal, 31 real-world networks were considered; tests show that LB--GDM performs significantly better than the state-of-the-art heuristics, while having a comparable or even lower computational complexity, which allows it to scale well even to large networks.\ldots{}

rawauthor:
Vincenza Carchiolo and Marco Grassia and Alessandro Longheu and Michele Malgeri and Giuseppe Mangioni

entry:
@Article{Carchiolo:2023:ENP,
  author =       "Vincenza Carchiolo and Marco Grassia and Alessandro
                 Longheu and Michele Malgeri and Giuseppe Mangioni",
  title =        "Efficient Node {PageRank} Improvement via
                 Link-building using Geometric Deep Learning",
  journal =      j-TKDD,
  volume =       "17",
  number =       "3",
  pages =        "38:1--38:??",
  month =        apr,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3551642",
  ISSN =         "1556-4681 (print), 1556-472X (electronic)",
  ISSN-L =       "1556-4681",
  bibdate =      "Fri Mar 31 09:53:45 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tkdd.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3551642",
  abstract =     "Centrality is a relevant topic in the field of network
                 research, due to its various theoretical and practical
                 implications. In general, all centrality metrics aim at
                 measuring the importance of nodes (according to some
                 definition of importance), and such importance scores
                 are used to rank the nodes in the network, therefore
                 the rank improvement is a strictly related topic. In a
                 given network, the rank improvement is achieved by
                 establishing new links, therefore the question shifts
                 to which and how many links should be collected to get
                 a desired rank. This problem, also known as
                 link-building has been shown to be NP-hard, and most
                 heuristics developed failed in obtaining good
                 performance with acceptable computational complexity.
                 In this article, we present LB--GDM, a novel approach
                 that leverages Geometric Deep Learning to tackle the
                 link-building problem. To validate our proposal, 31
                 real-world networks were considered; tests show that
                 LB--GDM performs significantly better than the
                 state-of-the-art heuristics, while having a comparable
                 or even lower computational complexity, which allows it
                 to scale well even to large networks.\ldots{}",
  acknowledgement = ack-nhfb,
  articleno =    "38",
  fjournal =     "ACM Transactions on Knowledge Discovery from Data
                 (TKDD)",
  journal-URL =  "https://dl.acm.org/loi/tkdd",
}


bibtype: article
filename: pagerank.bib
label: DSilva:2023:ISM
author: Jovi D'Silva and Uzzal Sharma
title: Impact of Similarity Measures in Graph-based Automatic Text Summarization of Konkani Texts
journal: j-TALLIP
volume: 22
number: 2
pages: 51:1--51:??
month: feb
monthnumber: 2
year: 2023
DOI: https://doi.org/10.1145/3554943
ISSN: 2375-4699 (print), 2375-4702 (electronic) OR 23754699 (print), 23754702 (electronic)
ISSNL: 2375-4699 OR 23754699
bibdate: Fri Mar 31 09:33:46 MDT 2023
bibtimestamp: 2023.03.31 09:33:46 MDT
URL: https://dl.acm.org/doi/10.1145/3554943
fjournal: ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)
acknowledgement: ack-nhfb
articleno: 51
journalURL: https://dl.acm.org/loi/tallip
rawauthor: Jovi D'Silva and Uzzal Sharma
rawtitle: Impact of Similarity Measures in Graph-based Automatic Text Summarization of {Konkani} Texts
bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tallip.bib

abstract:
Automatic text summarization is a popular area in Natural Language Processing and Machine Learning. In this work, we adopt a graph-based text summarization approach, using PageRank algorithm, for automatically summarizing Konkani text documents. Konkani, an Indo--Aryan language spoken primarily in the state of Goa, which is on the west coast of India. It is a low-resource language with limited language processing tools. Such tools are readily available in other popular languages of choice for automatic text summarization, like English. The Konkani language dataset used for this purpose is based on Konkani folktales. We examine the impact of various language-independent and language-dependent similarity measures on the construction of the graph. The language-dependent similarity measures use pre-trained fastText word embeddings. A fully connected undirected graph is constructed for each document with the sentences represented as the graph's vertices. The vertices are connected to each other based on how strongly they are related to one another. Thereafter, PageRank algorithm is used for ranking the scores of the vertices. The top-ranking sentences are used to generate the summary. ROUGE toolkit was used for evaluating the quality of these system-generated summaries, and the performance was evaluated against human generated ``gold-standard'' abstracts and also compared with baselines and benchmark systems. The experimental results show that language-independent similarity measures performed well compared to language-dependent similarity measures despite not using language-specific tools, such as stop-words list, stemming, and word embeddings.

rawabstract:
Automatic text summarization is a popular area in Natural Language Processing and Machine Learning. In this work, we adopt a graph-based text summarization approach, using PageRank algorithm, for automatically summarizing Konkani text documents. Konkani, an Indo--Aryan language spoken primarily in the state of Goa, which is on the west coast of India. It is a low-resource language with limited language processing tools. Such tools are readily available in other popular languages of choice for automatic text summarization, like English. The Konkani language dataset used for this purpose is based on Konkani folktales. We examine the impact of various language-independent and language-dependent similarity measures on the construction of the graph. The language-dependent similarity measures use pre-trained fastText word embeddings. A fully connected undirected graph is constructed for each document with the sentences represented as the graph's vertices. The vertices are connected to each other based on how strongly they are related to one another. Thereafter, PageRank algorithm is used for ranking the scores of the vertices. The top-ranking sentences are used to generate the summary. ROUGE toolkit was used for evaluating the quality of these system-generated summaries, and the performance was evaluated against human generated ``gold-standard'' abstracts and also compared with baselines and benchmark systems. The experimental results show that language-independent similarity measures performed well compared to language-dependent similarity measures despite not using language-specific tools, such as stop-words list, stemming, and word embeddings.

entry:
@Article{DSilva:2023:ISM,
  author =       "Jovi D'Silva and Uzzal Sharma",
  title =        "Impact of Similarity Measures in Graph-based Automatic
                 Text Summarization of {Konkani} Texts",
  journal =      j-TALLIP,
  volume =       "22",
  number =       "2",
  pages =        "51:1--51:??",
  month =        feb,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3554943",
  ISSN =         "2375-4699 (print), 2375-4702 (electronic)",
  ISSN-L =       "2375-4699",
  bibdate =      "Fri Mar 31 09:33:46 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tallip.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3554943",
  abstract =     "Automatic text summarization is a popular area in
                 Natural Language Processing and Machine Learning. In
                 this work, we adopt a graph-based text summarization
                 approach, using PageRank algorithm, for automatically
                 summarizing Konkani text documents. Konkani, an
                 Indo--Aryan language spoken primarily in the state of
                 Goa, which is on the west coast of India. It is a
                 low-resource language with limited language processing
                 tools. Such tools are readily available in other
                 popular languages of choice for automatic text
                 summarization, like English. The Konkani language
                 dataset used for this purpose is based on Konkani
                 folktales. We examine the impact of various
                 language-independent and language-dependent similarity
                 measures on the construction of the graph. The
                 language-dependent similarity measures use pre-trained
                 fastText word embeddings. A fully connected undirected
                 graph is constructed for each document with the
                 sentences represented as the graph's vertices. The
                 vertices are connected to each other based on how
                 strongly they are related to one another. Thereafter,
                 PageRank algorithm is used for ranking the scores of
                 the vertices. The top-ranking sentences are used to
                 generate the summary. ROUGE toolkit was used for
                 evaluating the quality of these system-generated
                 summaries, and the performance was evaluated against
                 human generated ``gold-standard'' abstracts and also
                 compared with baselines and benchmark systems. The
                 experimental results show that language-independent
                 similarity measures performed well compared to
                 language-dependent similarity measures despite not
                 using language-specific tools, such as stop-words list,
                 stemming, and word embeddings.",
  acknowledgement = ack-nhfb,
  articleno =    "51",
  fjournal =     "ACM Transactions on Asian and Low-Resource Language
                 Information Processing (TALLIP)",
  journal-URL =  "https://dl.acm.org/loi/tallip",
}


bibtype: article
filename: pagerank.bib
label: Huang:2023:TSP
author: Jun Huang and Gang Wu
journal: j-J-SCI-COMPUT
volume: 95
number: 1
pages: ??--??
month: apr
monthnumber: 4
year: 2023
CODEN: JSCOEB
DOI: https://doi.org/10.1007/s10915-023-02146-0
ISSN: 0885-7474 (print), 1573-7691 (electronic) OR 08857474 (print), 15737691 (electronic)
ISSNL: 0885-7474 OR 08857474
bibdate: Mon Apr 17 15:38:02 MDT 2023
bibtimestamp: 2023.04.17 15:38:02 MDT
URL: https://link.springer.com/article/10.1007/s10915-023-02146-0
fjournal: Journal of Scientific Computing
acknowledgement: ack-nhfb
ajournal: J. Sci. Comput.
articleno: 34
journalURL: http://link.springer.com/journal/10915
rawauthor: Jun Huang and Gang Wu
title:
Truncated and Sparse Power Methods with Partially Updating for Large and Sparse Higher-Order PageRank Problems

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

rawtitle:
Truncated and Sparse Power Methods with Partially Updating for Large and Sparse Higher-Order {PageRank} Problems

entry:
@Article{Huang:2023:TSP,
  author =       "Jun Huang and Gang Wu",
  title =        "Truncated and Sparse Power Methods with Partially
                 Updating for Large and Sparse Higher-Order {PageRank}
                 Problems",
  journal =      j-J-SCI-COMPUT,
  volume =       "95",
  number =       "1",
  pages =        "??--??",
  month =        apr,
  year =         "2023",
  CODEN =        "JSCOEB",
  DOI =          "https://doi.org/10.1007/s10915-023-02146-0",
  ISSN =         "0885-7474 (print), 1573-7691 (electronic)",
  ISSN-L =       "0885-7474",
  bibdate =      "Mon Apr 17 15:38:02 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jscicomput.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s10915-023-02146-0",
  acknowledgement = ack-nhfb,
  ajournal =     "J. Sci. Comput.",
  articleno =    "34",
  fjournal =     "Journal of Scientific Computing",
  journal-URL =  "http://link.springer.com/journal/10915",
}


bibtype: article
filename: pagerank.bib
label: Kim:2023:MMS
author: Whijin Kim and Hana Kim and Jihye Lee and Hyunji Kim and Ji-Hoon Kim
title: Multi-Mode SpMV Accelerator for Transprecision PageRank With Real-World Graphs
journal: j-IEEE-ACCESS
volume: 11
pages: 6261--6272
year: 2023
DOI: https://doi.org/10.1109/ACCESS.2023.3237079
ISSN: 2169-3536 OR 21693536
ISSNL: 2169-3536 OR 21693536
bibdate: Wed Oct 1 06:40:18 2025
bibtimestamp: 2025.10.01 06:40:18 ???
fjournal: IEEE Access
acknowledgement: ack-nhfb
journalURL: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639
rawauthor: Whijin Kim and Hana Kim and Jihye Lee and Hyunji Kim and Ji-Hoon Kim
rawtitle: Multi-Mode {SpMV} Accelerator for Transprecision {PageRank} With Real-World Graphs
bibsource:
https://www.math.utah.edu/pub/tex/bib/fparith.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

keywords:
floating-point multiplier; graph processing; half-precision (FP16); Hardware; Hypertext systems; Internet; Magnetic flux; PageRank (PR); single-precision (FP32); Social networking (online); Sparse matrices; sparse matrix vector multiplication (SpMV); Transprecision; Web search

entry:
@Article{Kim:2023:MMS,
  author =       "Whijin Kim and Hana Kim and Jihye Lee and Hyunji Kim
                 and Ji-Hoon Kim",
  title =        "Multi-Mode {SpMV} Accelerator for Transprecision
                 {PageRank} With Real-World Graphs",
  journal =      j-IEEE-ACCESS,
  volume =       "11",
  pages =        "6261--6272",
  year =         "2023",
  DOI =          "https://doi.org/10.1109/ACCESS.2023.3237079",
  ISSN =         "2169-3536",
  ISSN-L =       "2169-3536",
  bibdate =      "Wed Oct 1 06:40:18 2025",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/fparith.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE Access",
  journal-URL =  "https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639",
  keywords =     "floating-point multiplier; graph processing;
                 half-precision (FP16); Hardware; Hypertext systems;
                 Internet; Magnetic flux; PageRank (PR);
                 single-precision (FP32); Social networking (online);
                 Sparse matrices; sparse matrix vector multiplication
                 (SpMV); Transprecision; Web search",
}


bibtype: article
filename: pagerank.bib
label: Lai:2023:AAF
author: Fuqi Lai and Wen Li and Xiaofei Peng and Yannan Chen
title: Anderson accelerated fixed-point iteration for multilinear PageRank
journal: j-NUM-LIN-ALG-APPL
volume: 30
number: 5
pages: e2499:1--e2499:??
month: oct
monthnumber: 10
year: 2023
CODEN: NLAAEM
DOI: https://doi.org/10.1002/nla.2499
ISSN: 1070-5325 (print), 1099-1506 (electronic) OR 10705325 (print), 10991506 (electronic)
ISSNL: 1070-5325 OR 10705325
bibdate: Fri Nov 10 10:09:49 MST 2023
bibtimestamp: 2023.11.10 10:09:49 MST
fjournal: Numerical Linear Algebra with Applications
acknowledgement: ack-nhfb
ajournal: Numer. Linear Algebra Appl.
journalURL: https://onlinelibrary.wiley.com/journal/10991506
onlinedate: 28 March 2023
rawauthor: Fuqi Lai and Wen Li and Xiaofei Peng and Yannan Chen
rawtitle: {Anderson} accelerated fixed-point iteration for multilinear {PageRank}
entry:
@Article{Lai:2023:AAF,
  author =       "Fuqi Lai and Wen Li and Xiaofei Peng and Yannan Chen",
  title =        "{Anderson} accelerated fixed-point iteration for
                 multilinear {PageRank}",
  journal =      j-NUM-LIN-ALG-APPL,
  volume =       "30",
  number =       "5",
  pages =        "e2499:1--e2499:??",
  month =        oct,
  year =         "2023",
  CODEN =        "NLAAEM",
  DOI =          "https://doi.org/10.1002/nla.2499",
  ISSN =         "1070-5325 (print), 1099-1506 (electronic)",
  ISSN-L =       "1070-5325",
  bibdate =      "Fri Nov 10 10:09:49 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "Numer. Linear Algebra Appl.",
  fjournal =     "Numerical Linear Algebra with Applications",
  journal-URL =  "https://onlinelibrary.wiley.com/journal/10991506",
  onlinedate =   "28 March 2023",
}


bibtype: article
filename: pagerank.bib
label: Li:2023:ZWT
author: Yiming Li and Yanyan Shen and Lei Chen and Mingxuan Yuan
title: Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank
journal: j-PROC-VLDB-ENDOWMENT
volume: 16
number: 6
pages: 1332--1345
month: feb
monthnumber: 2
year: 2023
DOI: https://doi.org/10.14778/3583140.3583150
ISSN: 2150-8097 OR 21508097
bibdate: Mon May 1 07:43:11 MDT 2023
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbe.bib
bibtimestamp: 2023.05.01 07:43:11 MDT
URL: https://dl.acm.org/doi/10.14778/3583140.3583150
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: https://dl.acm.org/loi/pvldb
rawauthor: Yiming Li and Yanyan Shen and Lei Chen and Mingxuan Yuan
rawtitle: {Zebra}: When Temporal Graph Neural Networks Meet Temporal Personalized {PageRank}
abstract:
Temporal graph neural networks (T-GNNs) are state-of-the-art methods for learning representations over dynamic graphs. Despite the superior performance, T-GNNs still suffer from high computational complexity caused by the tedious recursive temporal ldots 

rawabstract:
Temporal graph neural networks (T-GNNs) are state-of-the-art methods for learning representations over dynamic graphs. Despite the superior performance, T-GNNs still suffer from high computational complexity caused by the tedious recursive temporal \ldots{}

entry:
@Article{Li:2023:ZWT,
  author =       "Yiming Li and Yanyan Shen and Lei Chen and Mingxuan
                 Yuan",
  title =        "{Zebra}: When Temporal Graph Neural Networks Meet
                 Temporal Personalized {PageRank}",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "16",
  number =       "6",
  pages =        "1332--1345",
  month =        feb,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.14778/3583140.3583150",
  ISSN =         "2150-8097",
  bibdate =      "Mon May 1 07:43:11 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  URL =          "https://dl.acm.org/doi/10.14778/3583140.3583150",
  abstract =     "Temporal graph neural networks (T-GNNs) are
                 state-of-the-art methods for learning representations
                 over dynamic graphs. Despite the superior performance,
                 T-GNNs still suffer from high computational complexity
                 caused by the tedious recursive temporal \ldots{}",
  acknowledgement = ack-nhfb,
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "https://dl.acm.org/loi/pvldb",
}


bibtype: article
filename: pagerank.bib
label: Liao:2023:TKE
author: Shengbin Liao and Zongkai Yang and Qingzhou Liao and Zhangxiong zheng
title: TopicLPRank: a keyphrase extraction method based on improved TopicRank
journal: j-J-SUPERCOMPUTING
volume: 79
number: 8
pages: 9073--9092
month: may
monthnumber: 5
year: 2023
CODEN: JOSUED
DOI: https://doi.org/10.1007/s11227-022-05022-0
ISSN: 0920-8542 (print), 1573-0484 (electronic) OR 09208542 (print), 15730484 (electronic)
ISSNL: 0920-8542 OR 09208542
bibdate: Thu Apr 6 06:16:05 MDT 2023
bibtimestamp: 2023.04.06 06:16:05 MDT
URL: https://link.springer.com/article/10.1007/s11227-022-05022-0
fjournal: The Journal of Supercomputing
acknowledgement: ack-nhfb
ajournal: J. Supercomputing
journalURL: http://link.springer.com/journal/11227
rawauthor: Shengbin Liao and Zongkai Yang and Qingzhou Liao and Zhangxiong zheng
rawtitle: {TopicLPRank}: a keyphrase extraction method based on improved {TopicRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/jsuper2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Liao:2023:TKE,
  author =       "Shengbin Liao and Zongkai Yang and Qingzhou Liao and
                 Zhangxiong zheng",
  title =        "{TopicLPRank}: a keyphrase extraction method based on
                 improved {TopicRank}",
  journal =      j-J-SUPERCOMPUTING,
  volume =       "79",
  number =       "8",
  pages =        "9073--9092",
  month =        may,
  year =         "2023",
  CODEN =        "JOSUED",
  DOI =          "https://doi.org/10.1007/s11227-022-05022-0",
  ISSN =         "0920-8542 (print), 1573-0484 (electronic)",
  ISSN-L =       "0920-8542",
  bibdate =      "Thu Apr 6 06:16:05 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jsuper2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s11227-022-05022-0",
  acknowledgement = ack-nhfb,
  ajournal =     "J. Supercomputing",
  fjournal =     "The Journal of Supercomputing",
  journal-URL =  "http://link.springer.com/journal/11227",
}


bibtype: article
filename: pagerank.bib
label: Pan:2023:PPD
author: Weifeng Pan and Hua Ming and Dae-Kyoo Kim and Zijiang Yang
journal: j-IEEE-TRANS-SOFTW-ENG
volume: 49
number: 3
pages: 1118--1151
month: mar
monthnumber: 3
year: 2023
CODEN: IESEDJ
DOI: https://doi.org/10.1109/TSE.2022.3171469
ISSN: 0098-5589 (print), 1939-3520 (electronic) OR 00985589 (print), 19393520 (electronic)
ISSNL: 0098-5589 OR 00985589
bibdate: Thu Mar 16 07:29:56 2023
bibtimestamp: 2023.03.16 07:29:56 ???
fjournal: IEEE Transactions on Software Engineering
acknowledgement: ack-nhfb
journalURL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32
rawauthor: Weifeng Pan and Hua Ming and Dae-Kyoo Kim and Zijiang Yang
title:
Pride: Prioritizing Documentation Effort Based on a PageRank-Like Algorithm and Simple Filtering Rules

bibsource:
https://www.math.utah.edu/pub/tex/bib/ieeetranssoftweng2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

rawtitle:
Pride: Prioritizing Documentation Effort Based on a {PageRank}-Like Algorithm and Simple Filtering Rules

entry:
@Article{Pan:2023:PPD,
  author =       "Weifeng Pan and Hua Ming and Dae-Kyoo Kim and Zijiang
                 Yang",
  title =        "Pride: Prioritizing Documentation Effort Based on a
                 {PageRank}-Like Algorithm and Simple Filtering Rules",
  journal =      j-IEEE-TRANS-SOFTW-ENG,
  volume =       "49",
  number =       "3",
  pages =        "1118--1151",
  month =        mar,
  year =         "2023",
  CODEN =        "IESEDJ",
  DOI =          "https://doi.org/10.1109/TSE.2022.3171469",
  ISSN =         "0098-5589 (print), 1939-3520 (electronic)",
  ISSN-L =       "0098-5589",
  bibdate =      "Thu Mar 16 07:29:56 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ieeetranssoftweng2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "IEEE Transactions on Software Engineering",
  journal-URL =  "http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=32",
}


bibtype: article
filename: pagerank.bib
label: Wang:2023:ESN
author: Hanzhi Wang and Zhewei Wei
title: Estimating Single-Node PageRank in $ tilde O(min d_t, sqrt m) $ Time
journal: j-PROC-VLDB-ENDOWMENT
volume: 16
number: 11
pages: 2949--2961
month: jul
monthnumber: 7
year: 2023
DOI: https://doi.org/10.14778/3611479.3611500
ISSN: 2150-8097 OR 21508097
bibdate: Fri Aug 25 07:25:43 MDT 2023
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbe.bib
bibtimestamp: 2023.08.25 07:25:43 MDT
URL: https://dl.acm.org/doi/10.14778/3611479.3611500
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
journalURL: https://dl.acm.org/loi/pvldb
rawauthor: Hanzhi Wang and Zhewei Wei
rawtitle: Estimating Single-Node {PageRank} in {$ \tilde {O}(\min d_t, \sqrt {m}) $} Time
abstract:
PageRank is a famous measure of graph centrality that has numerous applications in practice. The problem of computing a single node's PageRank has been the subject of extensive research over a decade. However, existing methods still incur large time complexities despite years of efforts. Even on undirected graphs where several valuable properties held by PageRank scores, the problem of locally approximating the PageRank score of a target node remains a challenging task. Two commonly adopted techniques, Monte-Carlo based random walks and backward push, both cost $O(n)$ time in the worst-case scenario, which hinders existing methods from achieving a sublinear time complexity like $O(sqrt m)$ on an undirected graph with $n$ nodes and $m$ edges.par In this paper, we focus on the problem of single-node PageRank computation on undirected graphs. We propose a novel algorithm, SetPush, for estimating single-node PageRank specifically on undirected graphs. With non-trivial analysis, we prove that our SetPush achieves the $tilde O(min (d_, sqrt m))$ time complexity for estimating the target node $t$'s PageRank with constant relative error and constant failure probability on undirected graphs. We conduct comprehensive experiments to demonstrate the effectiveness of SetPush.

rawabstract:
PageRank is a famous measure of graph centrality that has numerous applications in practice. The problem of computing a single node's PageRank has been the subject of extensive research over a decade. However, existing methods still incur large time complexities despite years of efforts. Even on undirected graphs where several valuable properties held by PageRank scores, the problem of locally approximating the PageRank score of a target node remains a challenging task. Two commonly adopted techniques, Monte-Carlo based random walks and backward push, both cost $O(n)$ time in the worst-case scenario, which hinders existing methods from achieving a sublinear time complexity like $O(\sqrt{m})$ on an undirected graph with $n$ nodes and $m$ edges.\par In this paper, we focus on the problem of single-node PageRank computation on undirected graphs. We propose a novel algorithm, SetPush, for estimating single-node PageRank specifically on undirected graphs. With non-trivial analysis, we prove that our SetPush achieves the $\tilde{O}(\min(d_, \sqrt{m}))$ time complexity for estimating the target node $t$'s PageRank with constant relative error and constant failure probability on undirected graphs. We conduct comprehensive experiments to demonstrate the effectiveness of SetPush.

entry:
@Article{Wang:2023:ESN,
  author =       "Hanzhi Wang and Zhewei Wei",
  title =        "Estimating Single-Node {PageRank} in {$ \tilde
                 {O}(\min d_t, \sqrt {m}) $} Time",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "16",
  number =       "11",
  pages =        "2949--2961",
  month =        jul,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.14778/3611479.3611500",
  ISSN =         "2150-8097",
  bibdate =      "Fri Aug 25 07:25:43 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  URL =          "https://dl.acm.org/doi/10.14778/3611479.3611500",
  abstract =     "PageRank is a famous measure of graph centrality that
                 has numerous applications in practice. The problem of
                 computing a single node's PageRank has been the subject
                 of extensive research over a decade. However, existing
                 methods still incur large time complexities despite
                 years of efforts. Even on undirected graphs where
                 several valuable properties held by PageRank scores,
                 the problem of locally approximating the PageRank score
                 of a target node remains a challenging task. Two
                 commonly adopted techniques, Monte-Carlo based random
                 walks and backward push, both cost $O(n)$ time in the
                 worst-case scenario, which hinders existing methods
                 from achieving a sublinear time complexity like
                 $O(\sqrt{m})$ on an undirected graph with $n$ nodes and
                 $m$ edges.\par

                 In this paper, we focus on the problem of single-node
                 PageRank computation on undirected graphs. We propose a
                 novel algorithm, SetPush, for estimating single-node
                 PageRank specifically on undirected graphs. With
                 non-trivial analysis, we prove that our SetPush
                 achieves the $\tilde{O}(\min(d_, \sqrt{m}))$ time
                 complexity for estimating the target node $t$'s
                 PageRank with constant relative error and constant
                 failure probability on undirected graphs.  We conduct
                 comprehensive experiments to demonstrate the
                 effectiveness of SetPush.",
  acknowledgement = ack-nhfb,
  ajournal =     "",
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "https://dl.acm.org/loi/pvldb",
}


bibtype: article
filename: pagerank.bib
label: Wen:2023:APM
author: Chun Wen and Qian-Ying Hu and Zhao-Li Shen
title: An adaptively preconditioned multi-step matrix splitting iteration for computing PageRank
journal: j-NUMER-ALGORITHMS
volume: 92
number: 2
pages: 1213--1231
month: feb
monthnumber: 2
year: 2023
CODEN: NUALEG
DOI: https://doi.org/10.1007/s11075-022-01337-4
ISSN: 1017-1398 (print), 1572-9265 (electronic) OR 10171398 (print), 15729265 (electronic)
ISSNL: 1017-1398 OR 10171398
bibdate: Mon Jan 30 12:22:10 MST 2023
bibtimestamp: 2023.01.30 12:22:10 MST
URL: https://link.springer.com/article/10.1007/s11075-022-01337-4
fjournal: Numerical Algorithms
acknowledgement: ack-nhfb
ajournal: Numer. Algorithms
journalURL: http://link.springer.com/journal/11075
rawauthor: Chun Wen and Qian-Ying Hu and Zhao-Li Shen
rawtitle: An adaptively preconditioned multi-step matrix splitting iteration for computing {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/numeralgorithms.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Wen:2023:APM,
  author =       "Chun Wen and Qian-Ying Hu and Zhao-Li Shen",
  title =        "An adaptively preconditioned multi-step matrix
                 splitting iteration for computing {PageRank}",
  journal =      j-NUMER-ALGORITHMS,
  volume =       "92",
  number =       "2",
  pages =        "1213--1231",
  month =        feb,
  year =         "2023",
  CODEN =        "NUALEG",
  DOI =          "https://doi.org/10.1007/s11075-022-01337-4",
  ISSN =         "1017-1398 (print), 1572-9265 (electronic)",
  ISSN-L =       "1017-1398",
  bibdate =      "Mon Jan 30 12:22:10 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/numeralgorithms.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s11075-022-01337-4",
  acknowledgement = ack-nhfb,
  ajournal =     "Numer. Algorithms",
  fjournal =     "Numerical Algorithms",
  journal-URL =  "http://link.springer.com/journal/11075",
}


bibtype: article
filename: pagerank.bib
label: Yan:2023:EFL
author: Yue Yan and Shujuan Jiang and Yanmei Zhang and Cheng Zhang
title: An effective fault localization approach based on PageRank and mutation analysis
journal: j-J-SYST-SOFTW
volume: 204
pages: ??--??
month: oct
monthnumber: 10
year: 2023
CODEN: JSSODM
DOI: https://doi.org/10.1016/j.jss.2023.111799
ISSN: 0164-1212 (print), 1873-1228 (electronic) OR 01641212 (print), 18731228 (electronic)
ISSNL: 0164-1212 OR 01641212
bibdate: Wed Sep 13 08:20:35 MDT 2023
bibtimestamp: 2023.09.13 08:20:35 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0164121223001942
fjournal: Journal of Systems and Software
acknowledgement: ack-nhfb
articleno: 111799
journalURL: http://www.sciencedirect.com/science/journal/01641212
rawauthor: Yue Yan and Shujuan Jiang and Yanmei Zhang and Cheng Zhang
rawtitle: An effective fault localization approach based on {PageRank} and mutation analysis
bibsource:
https://www.math.utah.edu/pub/tex/bib/jsystsoftw2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Yan:2023:EFL,
  author =       "Yue Yan and Shujuan Jiang and Yanmei Zhang and Cheng
                 Zhang",
  title =        "An effective fault localization approach based on
                 {PageRank} and mutation analysis",
  journal =      j-J-SYST-SOFTW,
  volume =       "204",
  number =       "??",
  pages =        "??--??",
  month =        oct,
  year =         "2023",
  CODEN =        "JSSODM",
  DOI =          "https://doi.org/10.1016/j.jss.2023.111799",
  ISSN =         "0164-1212 (print), 1873-1228 (electronic)",
  ISSN-L =       "0164-1212",
  bibdate =      "Wed Sep 13 08:20:35 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jsystsoftw2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0164121223001942",
  acknowledgement = ack-nhfb,
  articleno =    "111799",
  fjournal =     "Journal of Systems and Software",
  journal-URL =  "http://www.sciencedirect.com/science/journal/01641212",
}


bibtype: article
filename: pagerank.bib
label: Zhang:2023:PPA
author: Qi Zhang and Rongxia Tang and Zhengan Yao and Zan-Bo Zhang
title: A parallel PageRank algorithm for undirected graph
journal: j-APPL-MATH-COMP
volume: 459
pages: ??--??
day: 15
month: dec
monthnumber: 12
year: 2023
CODEN: AMHCBQ
DOI: https://doi.org/10.1016/j.amc.2023.128276
ISSN: 0096-3003 (print), 1873-5649 (electronic) OR 00963003 (print), 18735649 (electronic)
ISSNL: 0096-3003 OR 00963003
bibdate: Sat Aug 26 11:28:51 MDT 2023
bibtimestamp: 2023.08.26 11:28:51 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0096300323004459
fjournal: Applied Mathematics and Computation
acknowledgement: ack-nhfb
articleno: 128276
journalURL: http://www.sciencedirect.com/science/journal/00963003
rawauthor: Qi Zhang and Rongxia Tang and Zhengan Yao and Zan-Bo Zhang
rawtitle: A parallel {PageRank} algorithm for undirected graph
entry:
@Article{Zhang:2023:PPA,
  author =       "Qi Zhang and Rongxia Tang and Zhengan Yao and Zan-Bo
                 Zhang",
  title =        "A parallel {PageRank} algorithm for undirected graph",
  journal =      j-APPL-MATH-COMP,
  volume =       "459",
  number =       "??",
  pages =        "??--??",
  day =          "15",
  month =        dec,
  year =         "2023",
  CODEN =        "AMHCBQ",
  DOI =          "https://doi.org/10.1016/j.amc.2023.128276",
  ISSN =         "0096-3003 (print), 1873-5649 (electronic)",
  ISSN-L =       "0096-3003",
  bibdate =      "Sat Aug 26 11:28:51 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/applmathcomput2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0096300323004459",
  acknowledgement = ack-nhfb,
  articleno =    "128276",
  fjournal =     "Applied Mathematics and Computation",
  journal-URL =  "http://www.sciencedirect.com/science/journal/00963003",
}


bibtype: article
filename: pagerank.bib
label: Bentbib:2025:EMM
author: Abdeslem Hafid Bentbib and Maryam Boubekraoui and Khalide Jbilou
title: Extrapolation methods for multilinear PageRank
journal: j-NUMER-ALGORITHMS
volume: 98
number: 2
pages: 1013--1043
month: feb
monthnumber: 2
year: 2025
CODEN: NUALEG
DOI: https://doi.org/10.1007/s11075-024-01823-x
ISSN: 1017-1398 (print), 1572-9265 (electronic) OR 10171398 (print), 15729265 (electronic)
ISSNL: 1017-1398 OR 10171398
bibdate: Wed Jan 15 05:47:22 MST 2025
bibtimestamp: 2025.01.15 05:47:22 MST
URL: https://link.springer.com/article/10.1007/s11075-024-01823-x
fjournal: Numerical Algorithms
acknowledgement: ack-nhfb
ajournal: Numer. Algorithms
journalURL: http://link.springer.com/journal/11075
rawauthor: Abdeslem Hafid Bentbib and Maryam Boubekraoui and Khalide Jbilou
rawtitle: Extrapolation methods for multilinear {PageRank}
entry:
@Article{Bentbib:2025:EMM,
  author =       "Abdeslem Hafid Bentbib and Maryam Boubekraoui and
                 Khalide Jbilou",
  title =        "Extrapolation methods for multilinear {PageRank}",
  journal =      j-NUMER-ALGORITHMS,
  volume =       "98",
  number =       "2",
  pages =        "1013--1043",
  month =        feb,
  year =         "2025",
  CODEN =        "NUALEG",
  DOI =          "https://doi.org/10.1007/s11075-024-01823-x",
  ISSN =         "1017-1398 (print), 1572-9265 (electronic)",
  ISSN-L =       "1017-1398",
  bibdate =      "Wed Jan 15 05:47:22 MST 2025",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/numeralgorithms.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s11075-024-01823-x",
  acknowledgement = ack-nhfb,
  ajournal =     "Numer. Algorithms",
  fjournal =     "Numerical Algorithms",
  journal-URL =  "http://link.springer.com/journal/11075",
}


bibtype: article
filename: pagerank.bib
label: Chen:2024:MLP
author: Yannan Chen and Wen Li and Jingya Chang
title: Multi-Linear Pseudo-PageRank for Hypergraph Partitioning
journal: j-J-SCI-COMPUT
volume: 99
number: 1
pages: ??--??
month: apr
monthnumber: 4
year: 2024
CODEN: JSCOEB
DOI: https://doi.org/10.1007/s10915-024-02460-1
ISSN: 0885-7474 (print), 1573-7691 (electronic) OR 08857474 (print), 15737691 (electronic)
ISSNL: 0885-7474 OR 08857474
bibdate: Thu May 9 09:25:11 MDT 2024
bibtimestamp: 2024.05.09 09:25:11 MDT
URL: https://link.springer.com/article/10.1007/s10915-024-02460-1
fjournal: Journal of Scientific Computing
acknowledgement: ack-nhfb
ajournal: J. Sci. Comput.
articleno: 7
journalURL: http://link.springer.com/journal/10915
rawauthor: Yannan Chen and Wen Li and Jingya Chang
rawtitle: Multi-Linear Pseudo-{PageRank} for Hypergraph Partitioning
bibsource:
https://www.math.utah.edu/pub/tex/bib/jscicomput.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Chen:2024:MLP,
  author =       "Yannan Chen and Wen Li and Jingya Chang",
  title =        "Multi-Linear Pseudo-{PageRank} for Hypergraph
                 Partitioning",
  journal =      j-J-SCI-COMPUT,
  volume =       "99",
  number =       "1",
  pages =        "??--??",
  month =        apr,
  year =         "2024",
  CODEN =        "JSCOEB",
  DOI =          "https://doi.org/10.1007/s10915-024-02460-1",
  ISSN =         "0885-7474 (print), 1573-7691 (electronic)",
  ISSN-L =       "0885-7474",
  bibdate =      "Thu May 9 09:25:11 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jscicomput.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s10915-024-02460-1",
  acknowledgement = ack-nhfb,
  ajournal =     "J. Sci. Comput.",
  articleno =    "7",
  fjournal =     "Journal of Scientific Computing",
  journal-URL =  "http://link.springer.com/journal/10915",
}


bibtype: article
filename: pagerank.bib
label: Hu:2024:AEM
author: Qian-Ying Hu and Xian-Ming Gu and Chun Wen
title: Application of an extrapolation method in the Hessenberg algorithm for computing PageRank
journal: j-J-SUPERCOMPUTING
volume: 80
number: 15
pages: 22836--22859
month: oct
monthnumber: 10
year: 2024
CODEN: JOSUED
DOI: https://doi.org/10.1007/s11227-024-06327-y
ISSN: 0920-8542 (print), 1573-0484 (electronic) OR 09208542 (print), 15730484 (electronic)
ISSNL: 0920-8542 OR 09208542
bibdate: Tue Aug 13 06:33:54 MDT 2024
bibtimestamp: 2024.08.13 06:33:54 MDT
URL: https://link.springer.com/article/10.1007/s11227-024-06327-y
fjournal: The Journal of Supercomputing
acknowledgement: ack-nhfb
ajournal: J. Supercomputing
journalURL: http://link.springer.com/journal/11227
rawauthor: Qian-Ying Hu and Xian-Ming Gu and Chun Wen
rawtitle: Application of an extrapolation method in the {Hessenberg} algorithm for computing {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/jsuper2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Hu:2024:AEM,
  author =       "Qian-Ying Hu and Xian-Ming Gu and Chun Wen",
  title =        "Application of an extrapolation method in the
                 {Hessenberg} algorithm for computing {PageRank}",
  journal =      j-J-SUPERCOMPUTING,
  volume =       "80",
  number =       "15",
  pages =        "22836--22859",
  month =        oct,
  year =         "2024",
  CODEN =        "JOSUED",
  DOI =          "https://doi.org/10.1007/s11227-024-06327-y",
  ISSN =         "0920-8542 (print), 1573-0484 (electronic)",
  ISSN-L =       "0920-8542",
  bibdate =      "Tue Aug 13 06:33:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jsuper2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s11227-024-06327-y",
  acknowledgement = ack-nhfb,
  ajournal =     "J. Supercomputing",
  fjournal =     "The Journal of Supercomputing",
  journal-URL =  "http://link.springer.com/journal/11227",
}


bibtype: article
filename: pagerank.bib
label: Liu:2024:BEA
author: Haoyu Liu and Siqiang Luo
title: BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRank
journal: j-PROC-VLDB-ENDOWMENT
volume: 17
number: 9
pages: 2255--2268
month: may
monthnumber: 5
year: 2024
DOI: https://doi.org/10.14778/3665844.3665855
ISSN: 2150-8097 OR 21508097
ISSNL: 2150-8097 OR 21508097
bibdate: Wed Aug 7 06:07:54 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/vldbe.bib
bibtimestamp: 2024.08.07 06:07:54 MDT
URL: https://dl.acm.org/doi/10.14778/3665844.3665855
fjournal: Proceedings of the VLDB Endowment
acknowledgement: ack-nhfb
ajournal: Proc. VLDB Endowment
journalURL: https://dl.acm.org/loi/pvldb
rawauthor: Haoyu Liu and Siqiang Luo
rawtitle: {BIRD}: Efficient Approximation of Bidirectional Hidden Personalized {PageRank}
abstract:
In bipartite graph analysis, similarity measures play a pivotal role in various applications. Among existing metrics, the Bidirectional Hidden Personalized PageRank (BHPP) stands out for its superior query quality. However, the computational expense of ldots 

rawabstract:
In bipartite graph analysis, similarity measures play a pivotal role in various applications. Among existing metrics, the Bidirectional Hidden Personalized PageRank (BHPP) stands out for its superior query quality. However, the computational expense of \ldots{}

entry:
@Article{Liu:2024:BEA,
  author =       "Haoyu Liu and Siqiang Luo",
  title =        "{BIRD}: Efficient Approximation of Bidirectional
                 Hidden Personalized {PageRank}",
  journal =      j-PROC-VLDB-ENDOWMENT,
  volume =       "17",
  number =       "9",
  pages =        "2255--2268",
  month =        may,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.14778/3665844.3665855",
  ISSN =         "2150-8097",
  ISSN-L =       "2150-8097",
  bibdate =      "Wed Aug 7 06:07:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/vldbe.bib",
  URL =          "https://dl.acm.org/doi/10.14778/3665844.3665855",
  abstract =     "In bipartite graph analysis, similarity measures play
                 a pivotal role in various applications. Among existing
                 metrics, the Bidirectional Hidden Personalized PageRank
                 (BHPP) stands out for its superior query quality.
                 However, the computational expense of \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "Proc. VLDB Endowment",
  fjournal =     "Proceedings of the VLDB Endowment",
  journal-URL =  "https://dl.acm.org/loi/pvldb",
}


bibtype: article
filename: pagerank.bib
label: Ma:2024:PPP
author: Ke Ma and Jiawei Li and Mengyuan Zhao and Ibrahim Zamit and Bin Lin and Fei Guo and Jijun Tang
title: PPRTGI: a Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection
journal: j-TCBB
volume: 21
number: 3
pages: 480--491
month: may
monthnumber: 5
year: 2024
CODEN: ITCBCY
DOI: https://doi.org/10.1109/TCBB.2024.3374430
ISSN: 1545-5963 (print), 1557-9964 (electronic) OR 15455963 (print), 15579964 (electronic)
ISSNL: 1545-5963 OR 15455963
bibdate: Thu Sep 26 07:01:14 MDT 2024
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tcbb.bib
bibtimestamp: 2024.09.26 07:01:14 MDT
URL: https://dl.acm.org/doi/10.1109/TCBB.2024.3374430
fjournal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
acknowledgement: ack-nhfb
ajournal: IEEE/ACM Trans. Comput. Biol. Bioinform.
journalURL: https://dl.acm.org/loi/tcbb
rawauthor: Ke Ma and Jiawei Li and Mengyuan Zhao and Ibrahim Zamit and Bin Lin and Fei Guo and Jijun Tang
rawtitle: {PPRTGI}: a Personalized {PageRank} Graph Neural Network for {TF}-Target Gene Interaction Detection
abstract:
Transcription factors (TFs) regulation is required for the vast majority of biological processes in living organisms. Some diseases may be caused by improper transcriptional regulation. Identifying the target genes of TFs is thus critical for ldots 

rawabstract:
Transcription factors (TFs) regulation is required for the vast majority of biological processes in living organisms. Some diseases may be caused by improper transcriptional regulation. Identifying the target genes of TFs is thus critical for \ldots{}

entry:
@Article{Ma:2024:PPP,
  author =       "Ke Ma and Jiawei Li and Mengyuan Zhao and Ibrahim
                 Zamit and Bin Lin and Fei Guo and Jijun Tang",
  title =        "{PPRTGI}: a Personalized {PageRank} Graph Neural
                 Network for {TF}-Target Gene Interaction Detection",
  journal =      j-TCBB,
  volume =       "21",
  number =       "3",
  pages =        "480--491",
  month =        may,
  year =         "2024",
  CODEN =        "ITCBCY",
  DOI =          "https://doi.org/10.1109/TCBB.2024.3374430",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Thu Sep 26 07:01:14 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  URL =          "https://dl.acm.org/doi/10.1109/TCBB.2024.3374430",
  abstract =     "Transcription factors (TFs) regulation is required for
                 the vast majority of biological processes in living
                 organisms. Some diseases may be caused by improper
                 transcriptional regulation. Identifying the target
                 genes of TFs is thus critical for \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "IEEE/ACM Trans. Comput. Biol. Bioinform.",
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "https://dl.acm.org/loi/tcbb",
}


bibtype: article
filename: pagerank.bib
label: Meng:2025:MMS
author: Guang-Cong Meng and Yong-Xin Dong and Yue-Hua Feng
title: A modified multi-step splitting iteration and its variants for computing PageRank
journal: j-J-SUPERCOMPUTING
volume: 81
number: 1
pages: ??--??
month: jan
monthnumber: 1
year: 2025
CODEN: JOSUED
DOI: https://doi.org/10.1007/s11227-024-06669-7
ISSN: 0920-8542 (print), 1573-0484 (electronic) OR 09208542 (print), 15730484 (electronic)
ISSNL: 0920-8542 OR 09208542
bibdate: Thu Feb 13 07:09:33 MST 2025
bibtimestamp: 2025.02.13 07:09:33 MST
URL: https://link.springer.com/article/10.1007/s11227-024-06669-7
fjournal: The Journal of Supercomputing
acknowledgement: ack-nhfb
ajournal: J. Supercomputing
articleno: 186
journalURL: http://link.springer.com/journal/11227
rawauthor: Guang-Cong Meng and Yong-Xin Dong and Yue-Hua Feng
rawtitle: A modified multi-step splitting iteration and its variants for computing {PageRank}
bibsource:
https://www.math.utah.edu/pub/tex/bib/jsuper2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

entry:
@Article{Meng:2025:MMS,
  author =       "Guang-Cong Meng and Yong-Xin Dong and Yue-Hua Feng",
  title =        "A modified multi-step splitting iteration and its
                 variants for computing {PageRank}",
  journal =      j-J-SUPERCOMPUTING,
  volume =       "81",
  number =       "1",
  pages =        "??--??",
  month =        jan,
  year =         "2025",
  CODEN =        "JOSUED",
  DOI =          "https://doi.org/10.1007/s11227-024-06669-7",
  ISSN =         "0920-8542 (print), 1573-0484 (electronic)",
  ISSN-L =       "0920-8542",
  bibdate =      "Thu Feb 13 07:09:33 MST 2025",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jsuper2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s11227-024-06669-7",
  acknowledgement = ack-nhfb,
  ajournal =     "J. Supercomputing",
  articleno =    "186",
  fjournal =     "The Journal of Supercomputing",
  journal-URL =  "http://link.springer.com/journal/11227",
}


bibtype: article
filename: pagerank.bib
label: Qin:2024:PTP
author: Xinyi Qin and Lu Zhang and Min Liu and Guangzhong Liu
journal: j-TCBB
volume: 21
number: 6
pages: 1740--1751
year: 2024
CODEN: ITCBCY
DOI: https://doi.org/10.1109/TCBB.2024.3414497
ISSN: 1545-5963 (print), 1557-9964 (electronic) OR 15455963 (print), 15579964 (electronic)
ISSNL: 1545-5963 OR 15455963
bibdate: Sat Jan 18 06:37:18 MST 2025
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; https://www.math.utah.edu/pub/tex/bib/tcbb.bib
bibtimestamp: 2025.01.18 06:37:18 MST
URL: https://dl.acm.org/doi/10.1109/TCBB.2024.3414497
fjournal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
acknowledgement: ack-nhfb
ajournal: IEEE/ACM Trans. Comput. Biol. Bioinform.
journalURL: https://dl.acm.org/loi/tcbb
rawauthor: Xinyi Qin and Lu Zhang and Min Liu and Guangzhong Liu
title:
PRFold-TNN: Protein Fold Recognition With an Ensemble Feature Selection Method Using PageRank Algorithm Based on Transformer

abstract:
Understanding the tertiary structures of proteins is of great benefit to function in many aspects of human life. Protein fold recognition is a vital and salient means to know protein structure. Until now, researchers have successively proposed a variety ldots 

rawabstract:
Understanding the tertiary structures of proteins is of great benefit to function in many aspects of human life. Protein fold recognition is a vital and salient means to know protein structure. Until now, researchers have successively proposed a variety \ldots{}

rawtitle:
{PRFold-TNN}: Protein Fold Recognition With an Ensemble Feature Selection Method Using {PageRank} Algorithm Based on Transformer

entry:
@Article{Qin:2024:PTP,
  author =       "Xinyi Qin and Lu Zhang and Min Liu and Guangzhong
                 Liu",
  title =        "{PRFold-TNN}: Protein Fold Recognition With an
                 Ensemble Feature Selection Method Using {PageRank}
                 Algorithm Based on Transformer",
  journal =      j-TCBB,
  volume =       "21",
  number =       "6",
  pages =        "1740--1751",
  year =         "2024",
  CODEN =        "ITCBCY",
  DOI =          "https://doi.org/10.1109/TCBB.2024.3414497",
  ISSN =         "1545-5963 (print), 1557-9964 (electronic)",
  ISSN-L =       "1545-5963",
  bibdate =      "Sat Jan 18 06:37:18 MST 2025",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 https://www.math.utah.edu/pub/tex/bib/tcbb.bib",
  URL =          "https://dl.acm.org/doi/10.1109/TCBB.2024.3414497",
  abstract =     "Understanding the tertiary structures of proteins is
                 of great benefit to function in many aspects of human
                 life. Protein fold recognition is a vital and salient
                 means to know protein structure. Until now, researchers
                 have successively proposed a variety \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "IEEE/ACM Trans. Comput. Biol. Bioinform.",
  fjournal =     "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  journal-URL =  "https://dl.acm.org/loi/tcbb",
}


bibtype: article
filename: pagerank.bib
label: Shen:2024:PWF
journal: j-NUM-LIN-ALG-APPL
volume: 31
number: 3
pages: e2541:1--e2541:??
month: may
monthnumber: 5
year: 2024
CODEN: NLAAEM
DOI: https://doi.org/10.1002/nla.2541
ISSN: 1070-5325 (print), 1099-1506 (electronic) OR 10705325 (print), 10991506 (electronic)
ISSNL: 1070-5325 OR 10705325
bibdate: Tue May 28 13:42:15 MDT 2024
bibtimestamp: 2024.05.28 13:42:15 MDT
fjournal: Numerical Linear Algebra with Applications
acknowledgement: ack-nhfb
ajournal: Numer. Linear Algebra Appl.
journalURL: https://onlinelibrary.wiley.com/journal/10991506
onlinedate: 10 November 2023
author:
Zhao-Li Shen and Bruno Carpentieri and Chun Wen and Jian-Jun Wang and Stefano Serra-Capizzano and Shi-Ping Du

title:
Preconditioned weighted full orthogonalization method for solving singular linear systems from PageRank problems

rawauthor:
Zhao-Li Shen and Bruno Carpentieri and Chun Wen and Jian-Jun Wang and Stefano Serra-Capizzano and Shi-Ping Du

rawtitle:
Preconditioned weighted full orthogonalization method for solving singular linear systems from {PageRank} problems

entry:
@Article{Shen:2024:PWF,
  author =       "Zhao-Li Shen and Bruno Carpentieri and Chun Wen and
                 Jian-Jun Wang and Stefano Serra-Capizzano and Shi-Ping
                 Du",
  title =        "Preconditioned weighted full orthogonalization method
                 for solving singular linear systems from {PageRank}
                 problems",
  journal =      j-NUM-LIN-ALG-APPL,
  volume =       "31",
  number =       "3",
  pages =        "e2541:1--e2541:??",
  month =        may,
  year =         "2024",
  CODEN =        "NLAAEM",
  DOI =          "https://doi.org/10.1002/nla.2541",
  ISSN =         "1070-5325 (print), 1099-1506 (electronic)",
  ISSN-L =       "1070-5325",
  bibdate =      "Tue May 28 13:42:15 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/numlinaa.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "Numer. Linear Algebra Appl.",
  fjournal =     "Numerical Linear Algebra with Applications",
  journal-URL =  "https://onlinelibrary.wiley.com/journal/10991506",
  onlinedate =   "10 November 2023",
}


bibtype: article
filename: pagerank.bib
label: Yang:2024:SHP
author: Fei Yang and Huyin Zhang and Shiming Tao and Xiying Fan
title: Simple hierarchical PageRank graph neural networks
journal: j-J-SUPERCOMPUTING
volume: 80
number: 4
pages: 5509--5539
month: mar
monthnumber: 3
year: 2024
CODEN: JOSUED
DOI: https://doi.org/10.1007/s11227-023-05666-6
ISSN: 0920-8542 (print), 1573-0484 (electronic) OR 09208542 (print), 15730484 (electronic)
ISSNL: 0920-8542 OR 09208542
bibdate: Thu Feb 15 10:23:15 MST 2024
bibtimestamp: 2024.02.15 10:23:15 MST
URL: https://link.springer.com/article/10.1007/s11227-023-05666-6
fjournal: The Journal of Supercomputing
acknowledgement: ack-nhfb
ajournal: J. Supercomputing
journalURL: http://link.springer.com/journal/11227
rawauthor: Fei Yang and Huyin Zhang and Shiming Tao and Xiying Fan
rawtitle: Simple hierarchical {PageRank} graph neural networks
entry:
@Article{Yang:2024:SHP,
  author =       "Fei Yang and Huyin Zhang and Shiming Tao and Xiying
                 Fan",
  title =        "Simple hierarchical {PageRank} graph neural networks",
  journal =      j-J-SUPERCOMPUTING,
  volume =       "80",
  number =       "4",
  pages =        "5509--5539",
  month =        mar,
  year =         "2024",
  CODEN =        "JOSUED",
  DOI =          "https://doi.org/10.1007/s11227-023-05666-6",
  ISSN =         "0920-8542 (print), 1573-0484 (electronic)",
  ISSN-L =       "0920-8542",
  bibdate =      "Thu Feb 15 10:23:15 MST 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/jsuper2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib",
  URL =          "https://link.springer.com/article/10.1007/s11227-023-05666-6",
  acknowledgement = ack-nhfb,
  ajournal =     "J. Supercomputing",
  fjournal =     "The Journal of Supercomputing",
  journal-URL =  "http://link.springer.com/journal/11227",
}


bibtype: article
filename: pagerank.bib
label: Al-Eiadeh:2025:PDP
author: Mohammad Ryiad Al-Eiadeh and Mustafa Abdallah
journal: j-INT-J-INFO-SEC
volume: 24
number: 1
pages: ??--??
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bibdate: Wed Feb 19 07:15:59 MST 2025
bibtimestamp: 2025.02.19 07:15:59 MST
URL: https://link.springer.com/article/10.1007/s10207-024-00964-3
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acknowledgement: ack-nhfb
ajournal: Int. J. Info. Sci.
articleno: 50
journalURL: https://link.springer.com/journal/10207
rawauthor: Mohammad Ryiad Al-Eiadeh and Mustafa Abdallah
title:
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bibtype: article
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label: Boubekraoui:2025:ESM
author: Maryam Boubekraoui
title: Extrapolated splitting methods for multilinear PageRank computations
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volume: 208 (part A)
pages: 92--103
month: feb
monthnumber: 2
year: 2025
CODEN: ANMAEL
DOI: https://doi.org/10.1016/j.apnum.2023.11.019
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bibdate: Wed Dec 11 08:40:24 MST 2024
bibtimestamp: 2024.12.11 08:40:24 MST
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acknowledgement: ack-nhfb
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rawauthor: Maryam Boubekraoui
rawtitle: Extrapolated splitting methods for multilinear {PageRank} computations
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filename: pagerank.bib
label: Shen:2025:WDB
author: Zhao-Li Shen and Guo-Liang Han and Yu-Tong Liu and Bruno Carpentieri and Chun Wen and Jian-Jun Wang
journal: j-J-COMPUT-APPL-MATH
volume: 458
pages: ??--??
month: apr
monthnumber: 4
year: 2025
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2024.116332
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
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bibdate: Mon Dec 16 09:21:34 MST 2024
bibtimestamp: 2024.12.16 09:21:34 MST
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fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
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journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Zhao-Li Shen and Guo-Liang Han and Yu-Tong Liu and Bruno Carpentieri and Chun Wen and Jian-Jun Wang
title:
Weak dangling block reordering and multi-step block compression for efficiently computing and updating PageRank solutions

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bibtype: article
filename: pagerank.bib
label: Wang:2025:CEL
author: Pengwei Wang and Junye Qiao and Yuying Zhao and Zhijun Ding
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volume: 36
number: 2
pages: 185--196
month: feb
monthnumber: 2
year: 2025
CODEN: ITDSEO
DOI: https://doi.org/10.1109/TPDS.2024.3506625
ISSN: 1045-9219 (print), 1558-2183 (electronic) OR 10459219 (print), 15582183 (electronic)
ISSNL: 1045-9219 OR 10459219
bibdate: Wed Dec 18 16:51:49 2024
bibtimestamp: 2024.12.18 16:51:49 ???
fjournal: IEEE Transactions on Parallel and Distributed Systems
acknowledgement: ack-nhfb
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journalURL: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=71
rawauthor: Pengwei Wang and Junye Qiao and Yuying Zhao and Zhijun Ding
title:
Cost-Effective and Low-Latency Data Placement in Edge Environment Based on PageRank-Inspired Regional Value

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https://www.math.utah.edu/pub/tex/bib/ieeetranspardistsys2020.bib; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

keywords:
Cloud computing; Clouds; Correlation; Costs; Data centers; data placement; data temperature; Edge computing; Low latency communication; Memory; Optimization; regional value; Servers; Temperature distribution

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Cost-Effective and Low-Latency Data Placement in Edge Environment Based on {PageRank}-Inspired Regional Value

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bibtype: article
filename: pagerank.bib
label: Zhang:2025:TAN
author: Yu Zhang and Gang Wu
title: Two Accelerated Non-backtracking PageRank Algorithms for Large-scale Networks
journal: j-J-SCI-COMPUT
volume: 102
number: 1
pages: ??--??
month: jan
monthnumber: 1
year: 2025
CODEN: JSCOEB
DOI: https://doi.org/10.1007/s10915-024-02735-7
ISSN: 0885-7474 (print), 1573-7691 (electronic) OR 08857474 (print), 15737691 (electronic)
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bibdate: Thu Jan 16 08:20:12 MST 2025
bibtimestamp: 2025.01.16 08:20:12 MST
URL: https://link.springer.com/article/10.1007/s10915-024-02735-7
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acknowledgement: ack-nhfb
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articleno: 3
journalURL: http://link.springer.com/journal/10915
rawauthor: Yu Zhang and Gang Wu
rawtitle: Two Accelerated Non-backtracking {PageRank} Algorithms for Large-scale Networks
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bibtype: article
filename: pagerank.bib
label: Zhou:2025:MIF
author: Sheng-Wei Zhou and Chun Wen and Zhao-Li Shen and Bruno Carpentieri
title: The MFPIO iteration and the FPMPE method for multilinear PageRank computations
journal: j-J-COMPUT-APPL-MATH
volume: 454
pages: ??--??
day: 15
month: jan
monthnumber: 1
year: 2025
CODEN: JCAMDI
DOI: https://doi.org/10.1016/j.cam.2024.116192
ISSN: 0377-0427 (print), 1879-1778 (electronic) OR 03770427 (print), 18791778 (electronic)
ISSNL: 0377-0427 OR 03770427
bibdate: Fri Aug 23 08:18:50 MDT 2024
bibtimestamp: 2024.08.23 08:18:50 MDT
URL: http://www.sciencedirect.com/science/article/pii/S0377042724004412
fjournal: Journal of Computational and Applied Mathematics
acknowledgement: ack-nhfb
articleno: 116192
journalURL: http://www.sciencedirect.com/science/journal/03770427
rawauthor: Sheng-Wei Zhou and Chun Wen and Zhao-Li Shen and Bruno Carpentieri
rawtitle: The {MFPIO} iteration and the {FPMPE} method for multilinear {PageRank} computations
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  author =       "Sheng-Wei Zhou and Chun Wen and Zhao-Li Shen and Bruno
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  title =        "The {MFPIO} iteration and the {FPMPE} method for
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bibtype: proceedings
filename: pagerank.bib
label: ACM:2001:CPT
editor: ACM
publisher: pub-ACM
address: pub-ACM:adr
pages: xxii + 770
year: 2001
ISBN: 1-58113-348-0 OR 1581133480
ISBN13: 978-1-58113-348-6 OR 9781581133486
LCCN: TK5105.888 .I573 2001
bibdate: Mon May 10 14:10:25 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.loc.gov:7090/Voyager
bibtimestamp: 2010.05.10 14:10:25 MDT
URL: http://portal.acm.org/toc.cfm?id=511446
subject: World Wide Web; Congresses
acknowledgement: ack-nhfb
raweditor: {ACM}
booktitle:
Conference proceedings: the Tenth International World Wide Web Conference, Hong Kong, May 1--5, 2001

title:
Conference proceedings: the Tenth International World Wide Web Conference, Hong Kong, May 1--5, 2001

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bibtype: proceedings
filename: pagerank.bib
label: Bahill:2001:IIC
editor: Terry Bahill
publisher: pub-IEEE
address: pub-IEEE:adr
year: 2001
LCCN: TA168 .I18 2001
bibdate: Thu May 6 13:33:15 MDT 2010
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note: IEEE catalog number 01CH37236.
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2001 IEEE International Conference on Systems, Man and Cybernetics: October 7--10, 2001, Tucson Convention Center, Tucson, Arizona, USA

title:
2001 IEEE International Conference on Systems, Man and Cybernetics: October 7--10, 2001, Tucson Convention Center, Tucson, Arizona, USA

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bibtype: proceedings
filename: pagerank.bib
label: Croft:2001:PAI
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rawnote: ACM order number 606010. Special issue of the SIGIR Forum, {\bf 24} (2001).
booktitle:
Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval: New Orleans, Louisiana, USA, September 9--13, 2001

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filename: pagerank.bib
label: Anonymous:2002:PIW
editor: Anonymous
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ISBN13: 978-1-880672-20-4 OR 9781880672204
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Proceedings of the 11th International World Wide Web Conference: Sheraton Waikiki, Honolulu, Hawaii, 7--11 May 2002. WWW 2002

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  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
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  acknowledgement = ack-nhfb,
}


bibtype: proceedings
filename: pagerank.bib
label: WangLing:2002:PTI
editor: Tok Wang Ling and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 373
year: 2002
ISBN: 0-7695-1766-8 OR 0769517668
ISBN13: 978-0-7695-1766-7 OR 9780769517667
LCCN: TA168 .I583 200
bibdate: Thu May 6 13:57:37 MDT 2010
bibtimestamp: 2010.05.06 13:57:37 MDT
note: IEEE Computer Society order number PR01768.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=8419
subject: World Wide Web; Congresses; Internet; Systems engineering
acknowledgement: ack-nhfb
raweditor: Tok {Wang Ling} and others
rawnote: IEEE Computer Society order number PR01768.
booktitle:
Proceedings of the Third International Conference on Web Information Systems Engineering: Singapore, 12--14 December, 2002

title:
Proceedings of the Third International Conference on Web Information Systems Engineering: Singapore, 12--14 December, 2002

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


bibtype: proceedings
filename: pagerank.bib
label: Barbara:2003:PTS
editor: Daniel Barbara
publisher: pub-SIAM
address: pub-SIAM:adr
pages: xiii + 347
year: 2003
ISBN: 0-89871-545-8 OR 0898715458
ISBN13: 978-0-89871-545-3 OR 9780898715453
LCCN: QA76.9.D343
bibdate: Thu May 6 10:12:12 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.gbv.de:20011/gvk
bibtimestamp: 2010.05.06 10:12:12 MDT
acknowledgement: ack-nhfb
raweditor: Daniel Barbar{\'a}
booktitle:
Proceedings of the Third SIAM International Conference on Data Mining: [San Francisco, CA, May 1--3, 2003]

title:
Proceedings of the Third SIAM International Conference on Data Mining: [San Francisco, CA, May 1--3, 2003]

URL:
http://www.gbv.de/dms/bowker/toc/9780898715453; http://www.zentralblatt-math.org/zmath/en/search/?an=1076.68524

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


bibtype: proceedings
filename: pagerank.bib
label: Chick:2003:PWS
editor: Stephen E. Chick and others
publisher: pub-ACM
address: pub-ACM:adr
year: 2003
ISBN: 0-7803-8131-9 OR 0780381319
ISBN13: 978-0-7803-8131-5 OR 9780780381315
LCCN: QA76.5 .56 2003; QA76.9.C65 .W56 2003
bibdate: Thu May 6 13:44:36 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 13:44:36 MDT
note: ACM Order Number 578030. IEEE catalog number 03CH37499.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=8912
subject: digital computer simulation; congresses; simulation methods
acknowledgement: ack-nhfb
raweditor: Stephen E. Chick and others
rawnote: ACM Order Number 578030. IEEE catalog number 03CH37499.
booktitle:
Proceedings of the 2003 Winter Simulation Conference: Fairmont Hotel, New Orleans, LA, USA, December 7--10, 2003

title:
Proceedings of the 2003 Winter Simulation Conference: Fairmont Hotel, New Orleans, LA, USA, December 7--10, 2003

rawbooktitle:
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rawtitle:
{Proceedings of the 2003 Winter Simulation Conference: Fairmont Hotel, New Orleans, LA, USA, December 7--10, 2003}

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


bibtype: proceedings
filename: pagerank.bib
label: Helal:2003:SAI
editor: Abdelsalam A. Helal and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xiv + 430
year: 2003
ISBN: 0-7695-1872-9 OR 0769518729
ISBN13: 978-0-7695-1872-5 OR 9780769518725
LCCN: TK5105.875.I57 S95 2003
bibdate: Thu May 6 13:49:57 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 13:49:57 MDT
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=8426
subject: Internet; Congresses; Application software
acknowledgement: ack-nhfb
raweditor: Abdelsalam A. Helal and others
booktitle:
2003 Symposium on Applications and the Internet: proceedings: Orlando, Florida, January 27--31, 2003. SAINT 2003

title:
2003 Symposium on Applications and the Internet: proceedings: Orlando, Florida, January 27--31, 2003. SAINT 2003

rawbooktitle:
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rawtitle:
{2003 Symposium on Applications and the Internet: proceedings: Orlando, Florida, January 27--31, 2003. SAINT 2003}

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


bibtype: proceedings
filename: pagerank.bib
label: Hencsey:2003:PTI
editor: Gusztav Hencsey and Bebo White
publisher: pub-ACM
address: pub-ACM:adr
pages: xx + 752
year: 2003
ISBN: 1-58113-680-3 OR 1581136803
ISBN13: 978-1-58113-680-7 OR 9781581136807
LCCN: TK5105.888 .I58 2003
bibdate: Thu May 6 10:19:17 MDT 2010
bibtimestamp: 2010.05.06 10:19:17 MDT
URL: http://portal.acm.org/toc.cfm?id=775152
subject: World Wide Web; congresses; computer networks; hypertext systems; Internet
acknowledgement: ack-nhfb
raweditor: Guszt{\'a}v Hencsey and Bebo White
booktitle:
Proceedings of the Twelfth International Conference on World Wide Web: Budapest, Hungary, May 20--24, 2003

title:
Proceedings of the Twelfth International Conference on World Wide Web: Budapest, Hungary, May 20--24, 2003

bibsource:
https://www.math.utah.edu/pub/tex/bib/pagerank.bib; library.mit.edu:9909/mit01; z3950.gbv.de:20011/gvk

rawbooktitle:
{Proceedings of the Twelfth International Conference on World Wide Web: Budapest, Hungary, May 20--24, 2003}

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


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2003:IIS
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xii + 283
year: 2003
ISBN: 0-7695-1965-2 OR 0769519652
ISBN13: 978-0-7695-1965-4 OR 9780769519654
LCCN: QA76.9.D5 I157 2003
bibdate: Thu May 6 09:10:03 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 09:10:03 MDT
note: IEEE Computer Society order number PR01965.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=8591
subject: electronic data processing; distributed processing; congresses
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE Computer Society order number PR01965.
booktitle:
12th IEEE International Symposium on High Performance Distributed Computing: proceedings: 22--24 June, 2003, Seattle, Washington

title:
12th IEEE International Symposium on High Performance Distributed Computing: proceedings: 22--24 June, 2003, Seattle, Washington

rawbooktitle:
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rawtitle:
{12th IEEE International Symposium on High Performance Distributed Computing: proceedings: 22--24 June, 2003, Seattle, Washington}

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


bibtype: proceedings
filename: pagerank.bib
label: Liu:2003:ISW
editor: Jiming Liu and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxi + 730
year: 2003
ISBN: 0-7695-1932-6 OR 0769519326
ISBN13: 978-0-7695-1932-6 OR 9780769519326
LCCN: QA75.5 .I345 2003
bibdate: Thu May 6 09:07:36 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 09:07:36 MDT
URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8792
remark: Held jointly with IEEE/WIC International Conference on Intelligent Agent Technology.
subject: electronic data processing; congresses; artificial intelligence
acknowledgement: ack-nhfb
raweditor: Jiming Liu and others
booktitle:
IEEE / WIC International Conference on Web Intelligence, 2003, Halifax, NS, Canada. WI 2003. Proceedings

title:
IEEE / WIC International Conference on Web Intelligence, 2003. Halifax, NS, Canada. WI 2003. Proceedings

rawbooktitle:
{IEEE \slash WIC International Conference on Web Intelligence, 2003, Halifax, NS, Canada. WI 2003. Proceedings}

rawtitle:
{IEEE \slash WIC International Conference on Web Intelligence, 2003. Halifax, NS, Canada. WI 2003. Proceedings}

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


bibtype: proceedings
filename: pagerank.bib
label: Yang:2003:ICP
editor: Chu-Sing Yang and P. Sadayappan and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xvi + 647
year: 2003
ISBN: 0-7695-2017-0 OR 0769520170
ISBN13: 978-0-7695-2017-9 OR 9780769520179
LCCN: QA76.58 .I55 2003; QA76.6 .I548 2003
bibdate: Thu May 6 13:54:24 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 13:54:24 MDT
note: IEEE Computer Society order number PR02017.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=8782
subject: parallel processing (electronic computers); congresses
acknowledgement: ack-nhfb
raweditor: Chu-Sing Yang and P. Sadayappan and others
rawnote: IEEE Computer Society order number PR02017.
booktitle:
2003 International Conference on Parallel Processing: proceedings: 6--9 October, 2003, Kaohsiung, Taiwan

title:
2003 International Conference on Parallel Processing: proceedings: 6--9 October, 2003, Kaohsiung, Taiwan

rawbooktitle:
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rawtitle:
{2003 International Conference on Parallel Processing: proceedings: 6--9 October, 2003, Kaohsiung, Taiwan}

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


bibtype: proceedings
filename: pagerank.bib
label: Barolli:2004:ICA
editor: Leonard Barolli
publisher: pub-IEEE
address: pub-IEEE:adr
year: 2004
ISBN: 0-7695-2051-0 OR 0769520510
ISBN13: 978-0-7695-2051-3 OR 9780769520513
LCCN: TK5105.5 .I5616 2004
bibdate: Thu May 6 10:25:16 MDT 2010
bibtimestamp: 2010.05.06 10:25:16 MDT
note: IEEE Computer Society Order Number P2051.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=9028
acknowledgement: ack-nhfb
raweditor: Leonard Barolli
rawnote: IEEE Computer Society Order Number P2051.
booktitle:
18th International Conference on Advanced Information Networking and Applications, 2004. AINA 2004, 29--31 March 2004, [Fukuoka, Japan. Proceedings]

title:
18th International Conference on Advanced Information Networking and Applications, 2004. AINA 2004, 29--31 March 2004, [Fukuoka, Japan. Proceedings]

bibsource:
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rawbooktitle:
{18th International Conference on Advanced Information Networking and Applications, 2004. AINA 2004, 29--31 March 2004, [Fukuoka, Japan. Proceedings]}

rawtitle:
{18th International Conference on Advanced Information Networking and Applications, 2004. AINA 2004, 29--31 March 2004, [Fukuoka, Japan. Proceedings]}

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


bibtype: proceedings
filename: pagerank.bib
label: Ghorbani:2004:PAC
editor: Ali A. Ghorbani
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xi + 364
year: 2004
ISBN: 0-7695-2096-0 OR 0769520960
ISBN13: 978-0-7695-2096-4 OR 9780769520964
LCCN: TK5101.A1
bibdate: Thu May 6 10:28:50 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.gbv.de:20011/gvk
bibtimestamp: 2010.05.06 10:28:50 MDT
acknowledgement: ack-nhfb
raweditor: Ali A. Ghorbani
booktitle:
Proceedings of the 2nd Annual Communication Networks and Services Research Conference, 19--21 May 2004, Fredericton, New Brunswick

title:
Proceedings of the 2nd Annual Communication Networks and Services Research Conference, 19--21 May 2004, Fredericton, New Brunswick

URL:
http://ieeexplore.ieee.org/servlet/opac?punumber=9316; http://www.gbv.de/dms/bowker/toc/9780769520964

rawbooktitle:
{Proceedings of the 2nd Annual Communication Networks and Services Research Conference, 19--21 May 2004, Fredericton, New Brunswick}

rawtitle:
{Proceedings of the 2nd Annual Communication Networks and Services Research Conference, 19--21 May 2004, Fredericton, New Brunswick}

entry:
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  editor =       "Ali A. Ghorbani",
  booktitle =    "{Proceedings of the 2nd Annual Communication Networks
                 and Services Research Conference, 19--21 May 2004,
                 Fredericton, New Brunswick}",
  title =        "{Proceedings of the 2nd Annual Communication Networks
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                 Fredericton, New Brunswick}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xi + 364",
  year =         "2004",
  ISBN =         "0-7695-2096-0",
  ISBN-13 =      "978-0-7695-2096-4",
  LCCN =         "TK5101.A1",
  bibdate =      "Thu May 6 10:28:50 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.gbv.de:20011/gvk",
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bibtype: proceedings
filename: pagerank.bib
label: IEEE:2004:SWI
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxiii + 409
year: 2004
ISBN: 0-7695-2050-2 OR 0769520502
ISBN13: 978-0-7695-2050-6 OR 9780769520506
LCCN: TK5105.875.I57 S95 2004
bibdate: Thu May 6 09:13:55 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 09:13:55 MDT
note: IEEE Computer Society order number PR02050.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=8957
subject: Internet; congresses
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE Computer Society order number PR02050.
booktitle:
SAINT 2004 Workshops: 2004 International Symposium on Applications and the Internet: Workshops: proceedings: 26--30 January, 2004, Tokyo, Japan

title:
SAINT 2004 Workshops: 2004 International Symposium on Applications and the Internet: Workshops: proceedings: 26--30 January, 2004, Tokyo, Japan

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


bibtype: proceedings
filename: pagerank.bib
label: Leonardi:2004:AMW
editor: S. (Stefano) Leonardi
volume: 3243
publisher: pub-SV
address: pub-SV:adr
pages: viii + 187
year: 2004
ISBN: 3-540-23427-6 OR 3540234276
ISBN13: 978-3-540-23427-2 OR 9783540234272
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
LCCN: QA76.9.A43 W695 2004
bibdate: Thu May 6 12:25:46 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.loc.gov:7090/Voyager
bibtimestamp: 2010.05.06 12:25:46 MDT
series: ser-LNCS
subject: computer algorithms; congresses; data mining
acknowledgement: ack-nhfb
raweditor: S. (Stefano) Leonardi
booktitle:
Algorithms and models for the web-graph: third international workshop, WAW 2004, Rome, Italy, October 16, 2004: proceedings

title:
Algorithms and models for the web-graph: third international workshop, WAW 2004, Rome, Italy, October 16, 2004: proceedings

URL:
http://library.ust.hk/cgi/db/springer.scr?0302-9743/3243; http://www.loc.gov/catdir/enhancements/fy0823/2004113291-d.html

remark:
This volume contains papers presented at the 3rd Workshop on Algorithms and Models for the Web-Graph (WAW 2004) in conjunction with the 45th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2004).

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


bibtype: proceedings
filename: pagerank.bib
label: Zhong:2004:IWS
editor: Ning Zhong and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxviii + 811
year: 2004
ISBN: 0-7695-2100-2 OR 0769521002
ISBN13: 978-0-7695-2100-8 OR 9780769521008
LCCN: QA75.5 .I345 2004 .I429 2004; Q334 .I429 2004
bibdate: Thu May 6 14:08:43 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 14:08:43 MDT
note: IEEE Computer Society order number P2100.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=9689
subject: Artificial intelligence; Congresses
acknowledgement: ack-nhfb
raweditor: Ning Zhong and others
rawnote: IEEE Computer Society order number P2100.
booktitle:
IEEE / WIC / ACM International Conference on Web Intelligence (WI 2004): Beijing, China, September 20--24, 2004: proceedings

title:
IEEE / WIC / ACM International Conference on Web Intelligence (WI 2004): Beijing, China, September 20--24, 2004: proceedings

rawbooktitle:
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entry:
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  booktitle =    "{IEEE\slash WIC \slash ACM International Conference on
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  title =        "{IEEE\slash WIC \slash ACM International Conference on
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                 20--24, 2004: proceedings}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xxviii + 811",
  year =         "2004",
  ISBN =         "0-7695-2100-2",
  ISBN-13 =      "978-0-7695-2100-8",
  LCCN =         "QA75.5 .I345 2004 .I429 2004; Q334 .I429 2004",
  bibdate =      "Thu May 6 14:08:43 MDT 2010",
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  note =         "IEEE Computer Society order number P2100.",
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  acknowledgement = ack-nhfb,
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  subject =      "Artificial intelligence; Congresses",
}


bibtype: proceedings
filename: pagerank.bib
label: Barolli:2005:ICP
editor: Leonard Barolli and Jianhua Ma and Laurence Tianruo Yang
publisher: pub-IEEE
address: pub-IEEE:adr
year: 2005
ISBN: 0-7695-2281-5 OR 0769522815
ISBN13: 978-0-7695-2281-4 OR 9780769522814
LCCN: QA76.58 .I576 2005
bibdate: Thu May 6 09:16:01 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 09:16:01 MDT
note: IEEE Computer Society order number P2281.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10248
acknowledgement: ack-nhfb
raweditor: Leonard Barolli and Jianhua Ma and Laurence Tianruo Yang
rawnote: IEEE Computer Society order number P2281.
booktitle:
11th International Conference on Parallel and Distributed Systems, July 20--22, 2005, Fukuoka Institute of Technology (FIT), Fukuoka, Japan: proceedings

title:
11th International Conference on Parallel and Distributed Systems, July 20--22, 2005, Fukuoka Institute of Technology (FIT), Fukuoka, Japan: proceedings

subject:
electronic data processing; distributed processing; congresses; parallel processing (electronic computers)

rawbooktitle:
{11th International Conference on Parallel and Distributed Systems, July 20--22, 2005, Fukuoka Institute of Technology (FIT), Fukuoka, Japan: proceedings}

rawtitle:
{11th International Conference on Parallel and Distributed Systems, July 20--22, 2005, Fukuoka Institute of Technology (FIT), Fukuoka, Japan: proceedings}

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


bibtype: proceedings
filename: pagerank.bib
label: Han:2005:FII
editor: Jiawei Han and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxvii + 846
year: 2005
ISBN: 0-7695-2278-5 OR 0769522785
ISBN13: 978-0-7695-2278-4 OR 9780769522784
ISSN: 1550-4786 OR 15504786
LCCN: QA76.9.D343 I133 2005
bibdate: Thu May 6 15:15:08 MDT 2010
bibtimestamp: 2010.05.06 15:15:08 MDT
note: IEEE Computer Society Order Number P2278.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10470
acknowledgement: ack-nhfb
raweditor: Jiawei Han and others
rawnote: IEEE Computer Society Order Number P2278.
booktitle:
Fifth IEEE International Conference on Data Mining. ICDM 2005. 27--30 November 2005, Houston, Texas. Proceedings

title:
Fifth IEEE International Conference on Data Mining. ICDM 2005. 27--30 November 2005, Houston, Texas. Proceedings

bibsource:
fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.gbv.de:20011/gvk

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


bibtype: proceedings
filename: pagerank.bib
label: He:2005:TIC
editor: Xiangjian He and others
publisher: pub-IEEE
address: pub-IEEE:adr
year: 2005
ISBN: 0-7695-2316-1 OR 0769523161
ISBN13: 978-0-7695-2316-3 OR 9780769523163
LCCN: T58.5 .I545 2005
bibdate: Thu May 6 09:17:57 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 09:17:57 MDT
note: IEEE Computer Society Order Number P2316.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=9966
subject: information technology; congresses; application software
acknowledgement: ack-nhfb
raweditor: Xiangjian He and others
rawnote: IEEE Computer Society Order Number P2316.
booktitle:
Third International Conference on Information Technology and Applications (ICITA 2005): 4--7 July 2005, Sydney, Australia: proceedings

title:
Third International Conference on Information Technology and Applications (ICITA 2005): 4--7 July 2005, Sydney, Australia: proceedings

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


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2005:EIC
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xviii + 474
year: 2005
ISBN: 0-7695-2513-X OR 076952513X
ISBN13: 978-0-7695-2513-6 OR 9780769525136
LCCN: QA76.58 .E95 2006
bibdate: Thu May 6 10:35:20 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.loc.gov:7090/Voyager
bibtimestamp: 2010.05.06 10:35:20 MDT
note: IEEE Computer Society order number P2513.
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE Computer Society order number P2513.
booktitle:
14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing: proceedings: 15--17 February 2006: Montbeliard-Sochaux, France

title:
14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing: proceedings: 15--17 February 2006: Montbeliard-Sochaux, France

subject:
parallel programming (computer science); congresses; electronic data processing; distributed processing

rawbooktitle:
{14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing: proceedings: 15--17 February 2006: Montb{\'e}liard-Sochaux, France}

rawtitle:
{14th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing: proceedings: 15--17 February 2006: Montb{\'e}liard-Sochaux, France}

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


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2005:ICD
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xviii + 827
year: 2005
ISBN: 0-7695-2331-5 OR 0769523315
ISBN13: 978-0-7695-2331-6 OR 9780769523316
LCCN: QA76.9.D5 I57 2005
bibdate: Fri May 7 22:34:07 MDT 2010
bibtimestamp: 2010.05.07 22:34:07 MDT
note: IEEE Computer Society order number P2331.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=9816
subject: electronic data processing; distributed processing; congresses; computer networks
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE Computer Society order number P2331.
booktitle:
25th International Conference on Distributed Computing Systems: proceedings: 6--10 June, 2005, Columbus, Ohio, USA

title:
25th International Conference on Distributed Computing Systems: proceedings: 6--10 June, 2005, Columbus, Ohio, USA

bibsource:
alpha.lib.uwo.ca:210/INNOPAC; fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib

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


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2005:PII
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 10 + 843 + 11
year: 2005
ISBN: 0-7803-9361-9 OR 0780393619
ISBN13: 978-0-7803-9361-5 OR 9780780393615
LCCN: QA76.9.N38 I563 2005
bibdate: Fri May 7 19:25:20 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.loc.gov:7090/Voyager
bibtimestamp: 2010.05.07 19:25:20 MDT
note: IEEE Catalog Number: 05EX1156.
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE Catalog Number: 05EX1156.
booktitle:
Proceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering: (IEEE NLP-KE'05): October 30--November 1, 2005, Wuhan, China

title:
Proceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering: (IEEE NLP-KE'05): October 30--November 1, 2005, Wuhan, China

rawbooktitle:
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rawtitle:
{Proceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering: (IEEE NLP-KE'05): October 30--November 1, 2005, Wuhan, China}

entry:
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  editor =       "{IEEE}",
  booktitle =    "{Proceedings of 2005 IEEE International Conference on
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  title =        "{Proceedings of 2005 IEEE International Conference on
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  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "10 + 843 + 11",
  year =         "2005",
  ISBN =         "0-7803-9361-9",
  ISBN-13 =      "978-0-7803-9361-5",
  LCCN =         "QA76.9.N38 I563 2005",
  bibdate =      "Fri May 7 19:25:20 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
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  note =         "IEEE Catalog Number: 05EX1156.",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Natural Language
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                 China)",
}


bibtype: proceedings
filename: pagerank.bib
label: Meng:2005:IIC
editor: Max Meng and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 609
year: 2005
ISBN: 0-7803-9303-1 OR 0780393031
ISBN13: 978-0-7803-9303-5 OR 9780780393035
LCCN: TA165 .I57 2005
bibdate: Thu May 6 15:36:25 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.gbv.de:20011/gvk
bibtimestamp: 2010.05.06 15:36:25 MDT
note: IEEE catalog number 05EX1134.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10894
acknowledgement: ack-nhfb
raweditor: Max Meng and others
rawnote: IEEE catalog number 05EX1134.
booktitle:
IEEE International Conference on Information Acquisition, 2005. 27 June--3 July 2005, [the Chinese University of Hong Kong and the University of Macau]

title:
IEEE International Conference on Information Acquisition, 2005. 27 June--3 July 2005, [the Chinese University of Hong Kong and the University of Macau]

rawbooktitle:
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{IEEE International Conference on Information Acquisition, 2005. 27 June--3 July 2005, [the Chinese University of Hong Kong and the University of Macau]}

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


bibtype: proceedings
filename: pagerank.bib
label: Nesi:2005:FIC
editor: Paolo Nesi and Kia Ng and Jaime Delgado and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xi + 304
year: 2005
ISBN: 0-7695-2348-X OR 076952348X
ISBN13: 978-0-7695-2348-4 OR 9780769523484
LCCN: QA76.575 .I633 2005
bibdate: Thu May 6 15:56:58 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 15:56:58 MDT
note: IEEE Computer Society order number: P2348.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10605
subject: multimedia systems; congresses; music; data processing
acknowledgement: ack-nhfb
raweditor: Paolo Nesi and Kia Ng and Jaime Delgado and others
rawnote: IEEE Computer Society order number: P2348.
booktitle:
First International Conference on Automated Production of Cross Media Content for Multi-channel Distribution: proceedings: Florence, Italy, 30 November--2 December 2005

title:
First International Conference on Automated Production of Cross Media Content for Multi-channel Distribution: proceedings: Florence, Italy, 30 November--2 December 2005

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                 Distribution: proceedings: Florence, Italy, 30
                 November--2 December 2005}",
  title =        "{First International Conference on Automated
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                 Distribution: proceedings: Florence, Italy, 30
                 November--2 December 2005}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xi + 304",
  year =         "2005",
  ISBN =         "0-7695-2348-X",
  ISBN-13 =      "978-0-7695-2348-4",
  LCCN =         "QA76.575 .I633 2005",
  bibdate =      "Thu May 6 15:56:58 MDT 2010",
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  note =         "IEEE Computer Society order number: P2348.",
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  acknowledgement = ack-nhfb,
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  subject =      "multimedia systems; congresses; music; data
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}


bibtype: proceedings
filename: pagerank.bib
label: Skowron:2005:PIW
editor: Andrzej Skowron
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxxii + 819
year: 2005
ISBN: 0-7695-2415-X OR 076952415X
ISBN13: 978-0-7695-2415-3 OR 9780769524153
LCCN: TK5105.888 .I37 2005
bibdate: Thu May 6 16:19:56 MDT 2010
bibtimestamp: 2010.05.06 16:19:56 MDT
note: IEEE Computer Society Order Number P2415.
acknowledgement: ack-nhfb
raweditor: Andrzej Skowron
rawnote: IEEE Computer Society Order Number P2415.
booktitle:
Proceedings of the 2005 IEEE / WIC / ACM International Conference on Web Intelligence, 2005. September 19--22, 2005, Compiegne University of Technology, France

title:
Proceedings of the 2005 IEEE / WIC / ACM International Conference on Web Intelligence, 2005. September 19--22, 2005, Compiegne University of Technology, France

bibsource:
fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.gbv.de:20011/gvk

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  editor =       "Andrzej Skowron",
  booktitle =    "{Proceedings of the 2005 IEEE\slash WIC\slash ACM
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                 September 19--22, 2005, Compi{\`e}gne University of
                 Technology, France}",
  title =        "{Proceedings of the 2005 IEEE\slash WIC\slash ACM
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  publisher =    pub-IEEE,
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  bibdate =      "Thu May 6 16:19:56 MDT 2010",
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  note =         "IEEE Computer Society Order Number P2415.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10179;
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  acknowledgement = ack-nhfb,
}


bibtype: proceedings
filename: pagerank.bib
label: Barga:2006:IPI
editor: Roger S. Barga and Xiaofang Zhou
publisher: pub-IEEE
address: pub-IEEE:adr
year: 2006
ISBN: 0-7695-2571-7 OR 0769525717
ISBN13: 978-0-7695-2571-6 OR 9780769525716
LCCN: QA76.9.D3 I5582 2006
bibdate: Thu May 6 14:28:27 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 14:28:27 MDT
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10810
subject: database management; congresses; electronic data processing
acknowledgement: ack-nhfb
raweditor: Roger S. Barga and Xiaofang Zhou
booktitle:
ICDE '06: proceedings: 22nd International Conference on Data Engineering workshops: 3--7 April, 2006, Atlanta, Georgia

title:
ICDE '06: proceedings: 22nd International Conference on Data Engineering workshops: 3--7 April, 2006, Atlanta, Georgia

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


bibtype: proceedings
filename: pagerank.bib
label: Clifton:2006:SIC
editor: Christopher Wade Clifton and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxviii + 1221
year: 2006
ISBN: 0-7695-2702-7 OR 0769527027
ISBN13: 978-0-7695-2702-4 OR 9780769527024
LCCN: QA76.9.D343 I133 2006
bibdate: Thu May 6 15:43:32 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 15:43:32 MDT
note: IEEE Computer Society order number P2701.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=4053012
subject: data mining; congresses
acknowledgement: ack-nhfb
raweditor: Christopher Wade Clifton and others
rawnote: IEEE Computer Society order number P2701.
booktitle:
Sixth International Conference on Data Mining: ICDM 2006: proceedings: 18--22 December, 2006, Hong Kong

title:
Sixth International Conference on Data Mining: ICDM 2006: proceedings: 18--22 December, 2006, Hong Kong

rawbooktitle:
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{Sixth International Conference on Data Mining: ICDM 2006: proceedings: 18--22 December, 2006, Hong Kong}

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


bibtype: proceedings
filename: pagerank.bib
label: Feng:2006:IMM
editor: Huamin Feng and Shiqiang Yang and Yueting Zhuang
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 489 + 3
year: 2006
ISBN: 1-4244-0028-7 OR 1424400287
ISBN13: 978-1-4244-0028-7 OR 9781424400287
LCCN: QA76.575 .I6526 2006
bibdate: Thu May 6 14:44:51 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 14:44:51 MDT
note: IEEE catalog number: 06EX1249.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10988
subject: Multimedia systems; Congresses; Computer graphics
acknowledgement: ack-nhfb
raweditor: Huamin Feng and Shiqiang Yang and Yueting Zhuang
rawnote: IEEE catalog number: 06EX1249.
booktitle:
The 12th International MuIti-Media ModelIing Conference proceedings: MMM2006, 4--6 January 2006, Beijing, China

title:
The 12th International MuIti-Media ModelIing Conference proceedings: MMM2006, 4--6 January 2006, Beijing, China

rawbooktitle:
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{The 12th International MuIti-Media ModelIing Conference proceedings: MMM2006, 4--6 January 2006, Beijing, China}

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


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:AAC
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 489 + 3
year: 2006
ISBN: 1-4244-0716-8 OR 1424407168
ISBN13: 978-1-4244-0716-3 OR 9781424407163
LCCN: QA75.5 .I5745 2006eb
bibdate: Thu May 6 17:36:18 MDT 2010
bibtimestamp: 2010.05.06 17:36:18 MDT
note: IEEE catalog number 06EX1537C.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=4289832
subject: computers; congresses; computer systems; electronic data processing; autonomic computing
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE catalog number 06EX1537C.
booktitle:
ADCOM 2006: autonomic computing: proceedings: 2006 (14th) International Conference on Advanced Computing and Communications: December 20--23, 2006, National Institute of Technology Karnataka, Surathkal, India

title:
ADCOM 2006: autonomic computing: proceedings: 2006 (14th) International Conference on Advanced Computing and Communications: December 20--23, 2006, National Institute of Technology Karnataka, Surathkal, India

bibsource:
fsz3950.oclc.org:210/WorldCat; https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.lib.umich.edu:210/miu01_pub

rawbooktitle:
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rawtitle:
{ADCOM 2006: autonomic computing: proceedings: 2006 (14th) International Conference on Advanced Computing and Communications: December 20--23, 2006, National Institute of Technology Karnataka, Surathkal, India}

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  booktitle =    "{ADCOM 2006: autonomic computing: proceedings: 2006
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  title =        "{ADCOM 2006: autonomic computing: proceedings: 2006
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  address =      pub-IEEE:adr,
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  ISBN =         "1-4244-0716-8",
  ISBN-13 =      "978-1-4244-0716-3",
  LCCN =         "QA75.5 .I5745 2006eb",
  bibdate =      "Thu May 6 17:36:18 MDT 2010",
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                 https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
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  note =         "IEEE catalog number 06EX1537C.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4289832",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Advanced Computing and
                 Communications (14th: 2006: Karnataka, India)",
  subject =      "computers; congresses; computer systems; electronic
                 data processing; autonomic computing",
}


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:AIS
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xiv + 750
year: 2006
ISBN: 0-7695-2720-5, 0-7695-2362-5 OR 0769527205, 0769523625
ISBN13: 978-0-7695-2720-8, 978-0-7695-2362-0 OR 9780769527208, 9780769523620
LCCN: QA76 .S974 2006
bibdate: Thu May 6 08:30:22 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 08:30:22 MDT
note: IEEE Computer Society order number P2720.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=4031329
subject: electronic data processing; congresses; machine theory
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE Computer Society order number P2720.
booktitle:
47th Annual IEEE Symposium on Foundations of Computer Science: FOCS 2006: 21--24 October, 2006, Berkeley, California

title:
47th Annual IEEE Symposium on Foundations of Computer Science: FOCS 2006: 21--24 October, 2006, Berkeley, California

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  booktitle =    "{47th Annual IEEE Symposium on Foundations of Computer
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  title =        "{47th Annual IEEE Symposium on Foundations of Computer
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  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "xiv + 750",
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  LCCN =         "QA76 .S974 2006",
  bibdate =      "Thu May 6 08:30:22 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
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  note =         "IEEE Computer Society order number P2720.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4031329",
  acknowledgement = ack-nhfb,
  meetingname =  "Symposium on Foundations of Computer Science (47th:
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}


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:ASE
editor: IEEE
booktitle: 2006 Australian Software Engineering Conference: ASWEC 2006: 18--21 April, 2006, Sydney, Australia
title: 2006 Australian Software Engineering Conference: ASWEC 2006: 18--21 April, 2006, Sydney, Australia
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xv + 422
year: 2006
ISBN: 0-7695-2551-2 OR 0769525512
ISBN13: 978-0-7695-2551-8 OR 9780769525518
LCCN: QA76.758 .A89 2006
bibdate: Thu May 6 15:15:52 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 15:15:52 MDT
note: IEEE Computer Society order number P2251.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10753
subject: Software engineering; Congresses
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE Computer Society order number P2251.
rawbooktitle:
{2006 Australian Software Engineering Conference: ASWEC 2006: 18--21 April, 2006, Sydney, Australia}

rawtitle:
{2006 Australian Software Engineering Conference: ASWEC 2006: 18--21 April, 2006, Sydney, Australia}

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  ISBN =         "0-7695-2551-2",
  ISBN-13 =      "978-0-7695-2551-8",
  LCCN =         "QA76.758 .A89 2006",
  bibdate =      "Thu May 6 15:15:52 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE Computer Society order number P2251.",
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  acknowledgement = ack-nhfb,
  meetingname =  "Australian Software Engineering Conference (2006:
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bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:CIT
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: 936
year: 2006
ISBN: 0-7803-9741-X OR 078039741X
ISBN13: 978-0-7803-9741-5 OR 9780780397415
LCCN: TK5105; TK5105eb; Internet
bibdate: Thu May 6 14:38:56 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 14:38:56 MDT
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=4141327
subject: data transmission systems; congresses; information technology; Internet; signal processing
acknowledgement: ack-nhfb
raweditor: {IEEE}
booktitle:
Communications and Information Technologies, 2006. ISCIT '06, International Symposium on: Oct. 18 2006--Sept. 20 2006, [Bangkok, Thailand]

title:
Communications and Information Technologies, 2006. ISCIT '06, International Symposium on: Oct. 18 2006--Sept. 20 2006, [Bangkok, Thailand]

rawbooktitle:
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  booktitle =    "{Communications and Information Technologies, 2006.
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  title =        "{Communications and Information Technologies, 2006.
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  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  pages =        "936",
  year =         "2006",
  ISBN =         "0-7803-9741-X",
  ISBN-13 =      "978-0-7803-9741-5",
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  bibdate =      "Thu May 6 14:38:56 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
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  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=4141327",
  acknowledgement = ack-nhfb,
  meetingname =  "International Symposium on Communications and
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  subject =      "data transmission systems; congresses; information
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}


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:ICC
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxii + 2428
year: 2006
ISBN: 1-4244-0341-3 OR 1424403413
ISBN13: 978-1-4244-0341-7 OR 9781424403417
LCCN: TJ212.2 .I5474 2006
bibdate: Thu May 6 15:06:01 MDT 2010
bibtimestamp: 2010.05.06 15:06:01 MDT
note: IEEE catalog number 06EX1361.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=4149990
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE catalog number 06EX1361.
booktitle:
9th International Conference on Control, Automation, Robotics and Vision, 2006. ICARCV '06. 5--8 December 2006, Singapore

title:
9th International Conference on Control, Automation, Robotics and Vision, 2006. ICARCV '06. 5--8 December 2006, Singapore

rawbooktitle:
{9th International Conference on Control, Automation, Robotics and Vision, 2006. ICARCV '06. 5--8 December 2006, Singapore}

rawtitle:
{9th International Conference on Control, Automation, Robotics and Vision, 2006. ICARCV '06. 5--8 December 2006, Singapore}

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


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:IIM
editor: IEEE
booktitle: ICN 2006, ICONS 2006, MCL 2006: proceedings, Morne, Mauritius, 23--29 April, 2006
title: ICN 2006, ICONS 2006, MCL 2006: proceedings, Morne, Mauritius, 23--29 April, 2006
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxii + 2428
year: 2006
ISBN: 0-7695-2552-0 OR 0769525520
ISBN13: 978-0-7695-2552-5 OR 9780769525525
LCCN: See
bibdate: Thu May 6 17:38:14 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.lib.umich.edu:210/miu01_pub
bibtimestamp: 2010.05.06 17:38:14 MDT
note: IEEE Computer Society order number P2552.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10841
subject: computer networks; congresses; computer systems; mobile communication systems in education
acknowledgement: ack-nhfb
rawbooktitle: {ICN 2006, ICONS 2006, MCL 2006: proceedings, Morne, Mauritius, 23--29 April, 2006}
raweditor: {IEEE}
rawnote: IEEE Computer Society order number P2552.
rawtitle: {ICN 2006, ICONS 2006, MCL 2006: proceedings, Morne, Mauritius, 23--29 April, 2006}
entry:
@Proceedings{IEEE:2006:IIM,
  editor =       "{IEEE}",
  booktitle =    "{ICN 2006, ICONS 2006, MCL 2006: proceedings, Morne,
                 Mauritius, 23--29 April, 2006}",
  title =        "{ICN 2006, ICONS 2006, MCL 2006: proceedings, Morne,
                 Mauritius, 23--29 April, 2006}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  year =         "2006",
  ISBN =         "0-7695-2552-0",
  ISBN-13 =      "978-0-7695-2552-5",
  LCCN =         "See",
  bibdate =      "Thu May 6 17:38:14 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 z3950.lib.umich.edu:210/miu01_pub",
  note =         "IEEE Computer Society order number P2552.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10841",
  acknowledgement = ack-nhfb,
  meetingname =  "International Conference on Networking (5th: 2006:
                 Morne, Mauritius)",
  subject =      "computer networks; congresses; computer systems;
                 mobile communication systems in education",
}


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:IJC
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxii + 2428
year: 2006
ISBN: 0-7803-9490-9 OR 0780394909
ISBN13: 978-0-7803-9490-2 OR 9780780394902
LCCN: QA76.87 2006
bibdate: Thu May 6 16:16:00 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 16:16:00 MDT
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=11216
acknowledgement: ack-nhfb
raweditor: {IEEE}
booktitle:
2006 International Joint Conference on Neural Networks, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16--21, 2006

title:
2006 International Joint Conference on Neural Networks, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16--21, 2006

rawbooktitle:
{2006 International Joint Conference on Neural Networks, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16--21, 2006}

rawtitle:
{2006 International Joint Conference on Neural Networks, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16--21, 2006}

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


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:ISL
editor: IEEE
booktitle: 2006 IEEE Spoken Language Technology Workshop: Palm Beach, Aruba, 10--13 December 2006
title: 2006 IEEE Spoken Language Technology Workshop: Palm Beach, Aruba, 10--13 December 2006
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xiii + 261 + 2
year: 2006
ISBN: 1-4244-0872-5 (softbound edition) OR 1424408725 (softbound edition)
ISBN13: 978-1-4244-0872-6 (softbound edition) OR 9781424408726 (softbound edition)
LCCN: TK7895.S65
bibdate: Thu May 6 16:42:21 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.gbv.de:20011/gvk
bibtimestamp: 2010.05.06 16:42:21 MDT
note: IEEE catalog number 06EX1646.
acknowledgement: ack-nhfb
rawbooktitle: {2006 IEEE Spoken Language Technology Workshop: Palm Beach, Aruba, 10--13 December 2006}
raweditor: {IEEE}
rawnote: IEEE catalog number 06EX1646.
rawtitle: {2006 IEEE Spoken Language Technology Workshop: Palm Beach, Aruba, 10--13 December 2006}
subject:
automatic speech recognition; congresses; natural language processing (computer science); computational linguistics

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


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:ISP
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xvii + 478
year: 2006
ISBN: 0-7695-2554-7 OR 0769525547
ISBN13: 978-0-7695-2554-9 OR 9780769525549
LCCN: QA76.58. I578 2006
bibdate: Thu May 6 15:57:44 MDT 2010
bibtimestamp: 2010.05.06 15:57:44 MDT
note: IEEE Computer Society order number P2554.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=11156
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE Computer Society order number P2554.
booktitle:
International Symposium on Parallel Computing in Electrical Engineering, 2006. PARELEC 2006. 13--17 September 2006, Bialystok, Poland. Proceedings

title:
International Symposium on Parallel Computing in Electrical Engineering, 2006. PARELEC 2006. 13--17 September 2006, Bialystok, Poland. Proceedings

rawbooktitle:
{International Symposium on Parallel Computing in Electrical Engineering, 2006. PARELEC 2006. 13--17 September 2006, Bialystok, Poland. Proceedings}

rawtitle:
{International Symposium on Parallel Computing in Electrical Engineering, 2006. PARELEC 2006. 13--17 September 2006, Bialystok, Poland. Proceedings}

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


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:SIC
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxxi + 1215
year: 2006
ISBN: 1-4244-3075-5 OR 1424430755
ISBN13: 978-1-4244-3075-8 OR 9781424430758
bibdate: Thu May 6 16:06:29 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; z3950.gbv.de:20011/gvk
bibtimestamp: 2010.05.06 16:06:29 MDT
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10997
remark: Parallel als Druckausg. erschienen.
acknowledgement: ack-nhfb
raweditor: {IEEE}
booktitle:
Sixth International Conference on Advanced Learning Technologies, 2006. ICALT 2006. 5--7 July 2006, Kerkrade, The Netherlands. Proceedings

title:
Sixth International Conference on Advanced Learning Technologies, 2006. ICALT 2006 5--7 July 2006, Kerkrade, The Netherlands. Proceedings

rawbooktitle:
{Sixth International Conference on Advanced Learning Technologies, 2006. ICALT 2006. 5--7 July 2006, Kerkrade, The Netherlands. Proceedings}

rawtitle:
{Sixth International Conference on Advanced Learning Technologies, 2006. ICALT 2006 5--7 July 2006, Kerkrade, The Netherlands. Proceedings}

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


bibtype: proceedings
filename: pagerank.bib
label: IEEE:2006:WSW
editor: IEEE
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxxi + 1215
year: 2006
ISBN: 1-4244-0332-4 OR 1424403324
ISBN13: 978-1-4244-0332-5 OR 9781424403325
LCCN: TJ217.5 2006
bibdate: Thu May 6 09:24:59 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 09:24:59 MDT
note: IEEE catalog number 06EX1358C.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=11210
subject: intelligent control systems; congresses; automation
acknowledgement: ack-nhfb
raweditor: {IEEE}
rawnote: IEEE catalog number 06EX1358C.
booktitle:
WCICA 2006: Six World Congress on Intelligent Control and Automation: June 21--23, 2006, Dalian, China

title:
WCICA 2006: Six World Congress on Intelligent Control and Automation: June 21--23, 2006, Dalian, China

rawbooktitle:
{WCICA 2006: Six World Congress on Intelligent Control and Automation: June 21--23, 2006, Dalian, China}

rawtitle:
{WCICA 2006: Six World Congress on Intelligent Control and Automation: June 21--23, 2006, Dalian, China}

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


bibtype: proceedings
filename: pagerank.bib
label: Jeong:2006:SII
editor: Chang-Sung Jeong and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxiv + 276
year: 2006
ISBN: 0-7695-2687-X OR 076952687X
ISBN13: 978-0-7695-2687-4 OR 9780769526874
LCCN: T58.5 .I5662 2006
bibdate: Thu May 6 16:16:17 MDT 2010
bibtimestamp: 2010.05.06 16:16:17 MDT
note: IEEE Computer Society order number E2687.
URL: http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=4019822
acknowledgement: ack-nhfb
raweditor: Chang-Sung Jeong and others
rawnote: IEEE Computer Society order number E2687.
booktitle:
Sixth IEEE International Conference on Computer and Information Technology: CIT 2006. 20--22 September 2006, Korea University, Seoul, Korea

title:
Sixth IEEE International Conference on Computer and Information Technology: CIT 2006. 20--22 September 2006, Korea University, Seoul, Korea

rawbooktitle:
{Sixth IEEE International Conference on Computer and Information Technology: CIT 2006. 20--22 September 2006, Korea University, Seoul, Korea}

rawtitle:
{Sixth IEEE International Conference on Computer and Information Technology: CIT 2006. 20--22 September 2006, Korea University, Seoul, Korea}

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


bibtype: proceedings
filename: pagerank.bib
label: Nishida:2006:IWA
editor: T. (Toyoaki) Nishida and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxviii + 1085
year: 2006
ISBN: 0-7695-2747-7 OR 0769527477
ISBN13: 978-0-7695-2747-5 OR 9780769527475
LCCN: TK5105.888 2006
bibdate: Thu May 6 16:02:18 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; library.mit.edu:9909/mit01
bibtimestamp: 2010.05.06 16:02:18 MDT
note: IEEE Computer Society order number P2747.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=4061321
subject: World Wide Web; congresses; artificial intelligence; data mining
acknowledgement: ack-nhfb
raweditor: T. (Toyoaki) Nishida and others
rawnote: IEEE Computer Society order number P2747.
booktitle:
2006 IEEE / WIC / ACM International Conference on Web Intelligence: (WI 2006 main conference proceedings) (WI '06): proceedings: 18--22 December 2006, Hong Kong, China

title:
2006 IEEE / WIC / ACM International Conference on Web Intelligence: (WI 2006 main conference proceedings) (WI '06): proceedings: 18--22 December 2006, Hong Kong, China

rawbooktitle:
{2006 IEEE\slash WIC\slash ACM International Conference on Web Intelligence: (WI 2006 main conference proceedings) (WI '06): proceedings: 18--22 December 2006, Hong Kong, China}

rawtitle:
{2006 IEEE\slash WIC\slash ACM International Conference on Web Intelligence: (WI 2006 main conference proceedings) (WI '06): proceedings: 18--22 December 2006, Hong Kong, China}

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


bibtype: proceedings
filename: pagerank.bib
label: Perner:2006:ADM
editor: Petra Perner
volume: 4065
publisher: pub-SV
address: pub-SV:adr
pages: xi + 592
year: 2006
ISBN: 3-540-36036-0 OR 3540360360
ISBN13: 978-3-540-36036-0 OR 9783540360360
ISSN: 0302-9743 (print), 1611-3349 (electronic) OR 03029743 (print), 16113349 (electronic)
LCCN: QA76.9.D343
bibdate: Thu May 06 17:11:23 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; prodorbis.library.yale.edu:7090/voyager
bibtimestamp: 2010.05.06 17:11:23 ???
series: ser-LNCS
subject: Data mining; Congresses
acknowledgement: ack-nhfb
raweditor: Petra Perner
booktitle:
Advances in data mining: applications in medicine, web mining, marketing, image and signal mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14--15, 2006: proceedings

title:
Advances in data mining: applications in medicine, web mining, marketing, image and signal mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14--15, 2006: proceedings

rawbooktitle:
{Advances in data mining: applications in medicine, web mining, marketing, image and signal mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14--15, 2006: proceedings}

rawtitle:
{Advances in data mining: applications in medicine, web mining, marketing, image and signal mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14--15, 2006: proceedings}

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


bibtype: proceedings
filename: pagerank.bib
label: Turner:2006:SII
editor: Stephen John Turner and Bu Sung Lee and Wientong Cai
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxiii + 662
year: 2006
ISBN: 0-7695-2585-7 OR 0769525857
ISBN13: 978-0-7695-2585-3 OR 9780769525853
LCCN: QA76.9.C58 I133 2006
bibdate: Thu May 6 14:57:13 MDT 2010
bibtimestamp: 2010.05.06 14:57:13 MDT
note: IEEE Computer Society Order Number P2585.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10857
acknowledgement: ack-nhfb
raweditor: Stephen John Turner and Bu Sung Lee and Wientong Cai
rawnote: IEEE Computer Society Order Number P2585.
booktitle:
Sixth IEEE International Symposium on Cluster Computing and the Grid workshops, 2006: CCGrid 06. 16--19 May 2006, Singapore

title:
Sixth IEEE International Symposium on Cluster Computing and the Grid workshops, 2006: CCGrid 06. 16--19 May 2006, Singapore

rawbooktitle:
{Sixth IEEE International Symposium on Cluster Computing and the Grid workshops, 2006: CCGrid 06. 16--19 May 2006, Singapore}

rawtitle:
{Sixth IEEE International Symposium on Cluster Computing and the Grid workshops, 2006: CCGrid 06. 16--19 May 2006, Singapore}

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


bibtype: proceedings
filename: pagerank.bib
label: Wombacher:2006:JCC
editor: Andreas Wombacher and Christian Huemer and Markus Stolze and others
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxiv + 588
year: 2006
ISBN: 0-7695-2511-3 OR 0769525113
ISBN13: 978-0-7695-2511-2 OR 9780769525112
LCCN: HF5548.32 .I57 2006
bibdate: Thu May 6 15:53:12 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 15:53:12 MDT
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10920
subject: electronic commerce; congresses; organizational change
acknowledgement: ack-nhfb
raweditor: Andreas Wombacher and Christian Huemer and Markus Stolze and others
booktitle:
Joint Conference: CEC / EEE 2006 Joint Conference: 8th IEEE International Conference on E-Commerce and Technology (CEC 2006): 3rd IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (EEE 2006): 3rd IEEE International Workshop on Mobile Commerce and Wireless Services (WMCS 2006): Joint Workshop: 2nd International Workshop on Business Service Networks (BSN 2006): 2nd International Workshop on Service Oriented Solutions for Cooperative Organizations (SoS4CO ): June 26--29, 2006, San Francisco, California

title:
Joint Conference: CEC / EEE 2006 Joint Conference: 8th IEEE International Conference on E-Commerce and Technology (CEC 2006): 3rd IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (EEE 2006): 3rd IEEE International Workshop on Mobile Commerce and Wireless Services (WMCS 2006): Joint Workshop: 2nd International Workshop on Business Service Networks (BSN 2006): 2nd International Workshop on Service Oriented Solutions for Cooperative Organizations (SoS4CO ): June 26--29, 2006, San Francisco, California

rawbooktitle:
{Joint Conference: CEC\slash EEE 2006 Joint Conference: 8th IEEE International Conference on E-Commerce and Technology (CEC 2006): 3rd IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (EEE 2006): 3rd IEEE International Workshop on Mobile Commerce and Wireless Services (WMCS 2006): Joint Workshop: 2nd International Workshop on Business Service Networks (BSN 2006): 2nd International Workshop on Service Oriented Solutions for Cooperative Organizations (SoS4CO ): June 26--29, 2006, San Francisco, California}

rawtitle:
{Joint Conference: CEC\slash EEE 2006 Joint Conference: 8th IEEE International Conference on E-Commerce and Technology (CEC 2006): 3rd IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (EEE 2006): 3rd IEEE International Workshop on Mobile Commerce and Wireless Services (WMCS 2006): Joint Workshop: 2nd International Workshop on Business Service Networks (BSN 2006): 2nd International Workshop on Service Oriented Solutions for Cooperative Organizations (SoS4CO ): June 26--29, 2006, San Francisco, California}

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


bibtype: proceedings
filename: pagerank.bib
label: Zhang:2006:IIC
editor: Yan-Qing Zhang and Tsau Y. Lin
booktitle: 2006 IEEE International Conference on Granular Computing: Atlanta, USA, May 10--12, 2006
title: 2006 IEEE International Conference on Granular Computing: Atlanta, USA, May 10--12, 2006
publisher: pub-IEEE
address: pub-IEEE:adr
pages: xxiv + 588
year: 2006
ISBN: 1-4244-0134-8 OR 1424401348
ISBN13: 978-1-4244-0134-5 OR 9781424401345
LCCN: QA76.9.S63 2006
bibdate: Thu May 6 09:54:29 MDT 2010
bibsource: https://www.math.utah.edu/pub/tex/bib/pagerank.bib; melvyl.cdlib.org:210/CDL90
bibtimestamp: 2010.05.06 09:54:29 MDT
note: IEEE catalog number 06EX1286.
URL: http://ieeexplore.ieee.org/servlet/opac?punumber=10898
subject: granular computing; congresses
acknowledgement: ack-nhfb
rawbooktitle: {2006 IEEE International Conference on Granular Computing: Atlanta, USA, May 10--12, 2006}
raweditor: Yan-Qing Zhang and Tsau Y. Lin
rawnote: IEEE catalog number 06EX1286.
rawtitle: {2006 IEEE International Conference on Granular Computing: Atlanta, USA, May 10--12, 2006}
entry:
@Proceedings{Zhang:2006:IIC,
  editor =       "Yan-Qing Zhang and Tsau Y. Lin",
  booktitle =    "{2006 IEEE International Conference on Granular
                 Computing: Atlanta, USA, May 10--12, 2006}",
  title =        "{2006 IEEE International Conference on Granular
                 Computing: Atlanta, USA, May 10--12, 2006}",
  publisher =    pub-IEEE,
  address =      pub-IEEE:adr,
  year =         "2006",
  ISBN =         "1-4244-0134-8",
  ISBN-13 =      "978-1-4244-0134-5",
  LCCN =         "QA76.9.S63 2006",
  bibdate =      "Thu May 6 09:54:29 MDT 2010",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/pagerank.bib;
                 melvyl.cdlib.org:210/CDL90",
  note =         "IEEE catalog number 06EX1286.",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10898",
  acknowledgement = ack-nhfb,
  meetingname =  "IEEE International Conference on Granular Computing
                 (2006: Atlanta, Ga.)",
  subject =      "granular computing; congresses",
}


