poster

Pagerank based clustering of hypertext document collections

Authors:

Konstantin Avrachenkov,

Vladimir Dobrynin,

Danil Nemirovsky,

Son Kim Pham,

Elena SmirnovaAuthors Info & Claims

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

Pages 873 - 874

https://doi.org/10.1145/1390334.1390549

Published: 20 July 2008 Publication History

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Abstract

Clustering hypertext document collection is an important task in Information Retrieval. Most clustering methods are based on document content and do not take into account the hyper-text links. Here we propose a novel PageRank based clustering (PRC) algorithm which uses the hypertext structure. The PRC algorithm produces graph partitioning with high modularity and coverage. The comparison of the PRC algorithm with two content based clustering algorithms shows that there is a good match between PRC clustering and content based clustering.

References

[1]

R. Andersen, F. Chung and K. Lang, "Local partitioning for directed graphs using PageRank", WAW2007.

Digital Library

Google Scholar

[2]

U. Brandes, M. Gaertler and D. Wagner, "Experiments on graph clustering algorithms", ESA'2003.

Google Scholar

[3]

V. Dobrynin, D. Patterson and N. Rooney, "Contextual Document Clustering", LNCS v.2997, 2004.

Google Scholar

[4]

V. Dobrynin, D. Patterson and N. Rooney, "Contextual Document Clustering", LNCS v.2997, 2004.

Google Scholar

[5]

D. Gibson, J.M. Kleinberg and P. Raghavan, "Inferring Web Communities from Link Topology", The 9th ACM Conference on Hypertext and Hypermedia, 1998.

Digital Library

Google Scholar

[6]

T.H. Haveliwala, "Topic-sensitive PageRank", WAW 2002.

Digital Library

Google Scholar

[7]

J. Huang, T. Zhu and D. Schuurmans, "Web Communities Identification from Random Walks", LNCS v.4213, 2006.

Digital Library

Google Scholar

[8]

M. Meila and W. Pentney, "Clustering by weighted cuts in directed graphs", SDM 2007.

Crossref

Google Scholar

[9]

M.E.J. Newman, "Modularity and community structure in networks", PNAS, v.103, no.23, 2006.

Google Scholar

[10]

L. Page, S. Brin, R. Motwani and T. Winograd, "The PageRank citation ranking: Bringing order to the Web", Technical Report, Stanford, 1998.

Google Scholar

Cited By

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Chen SBhatnagar R(2023)Community Detection in Large Directed GraphsBig Data Analytics10.1007/978-3-031-24094-2_12(172-181)Online publication date: 29-Jan-2023
https://doi.org/10.1007/978-3-031-24094-2_12
Meng LYuan LChen ZLin XYang S(2022)Index-based Structural Clustering on Directed Graphs2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00257(2831-2844)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00257
Yuan MZobel JLin P(2022)Measurement of clustering effectiveness for document collectionsInformation Retrieval Journal10.1007/s10791-021-09401-825:3(239-268)Online publication date: 10-Jan-2022
https://doi.org/10.1007/s10791-021-09401-8
Show More Cited By

Index Terms

Pagerank based clustering of hypertext document collections
1. Information systems
  1. Information retrieval

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Information

Published In

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

July 2008

934 pages

ISBN:9781605581644

DOI:10.1145/1390334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Mun-Kew Leong
National Library Board, Singapore
,
Program Chairs:
Syung Hyon Myaeng
Information and Communications University, Korea
,
Douglas W. Oard
University of Maryland, College Park, USA
,
Fabrizio Sebastiani
Consiglio Nazionale delle Ricerche, Italy

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Association for Computing Machinery

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Publication History

Published: 20 July 2008

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SIGIR '08: The 31st Annual International ACM SIGIR Conference

July 20 - 24, 2008

Singapore, Singapore

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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Chen SBhatnagar R(2023)Community Detection in Large Directed GraphsBig Data Analytics10.1007/978-3-031-24094-2_12(172-181)Online publication date: 29-Jan-2023
https://doi.org/10.1007/978-3-031-24094-2_12
Meng LYuan LChen ZLin XYang S(2022)Index-based Structural Clustering on Directed Graphs2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00257(2831-2844)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00257
Yuan MZobel JLin P(2022)Measurement of clustering effectiveness for document collectionsInformation Retrieval Journal10.1007/s10791-021-09401-825:3(239-268)Online publication date: 10-Jan-2022
https://doi.org/10.1007/s10791-021-09401-8
Avrachenkov KMironov M(2021)Cluster-size constrained network partitioning2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9412095(10058-10065)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9412095
Bautista EAbry PGonçalves P(2019)Lγ-PageRank for semi-supervised learningApplied Network Science10.1007/s41109-019-0172-x4:1Online publication date: 19-Aug-2019
https://doi.org/10.1007/s41109-019-0172-x
Avrachenkov KKondratev AMazalov VRubanov D(2018)Network partitioning algorithms as cooperative gamesComputational Social Networks10.1186/s40649-018-0059-55:1Online publication date: 28-Oct-2018
https://doi.org/10.1186/s40649-018-0059-5
HONG SPARK KLIM CCHANG J(2017)A New Efficient Resource Management Framework for Iterative MapReduce Processing in Large-Scale Data AnalysisIEICE Transactions on Information and Systems10.1587/transinf.2016DAP0013E100.D:4(704-717)Online publication date: 2017
https://doi.org/10.1587/transinf.2016DAP0013
Avrachenkov KKondratev AMazalov V(2017)Cooperative Game Theory Approaches for Network PartitioningComputing and Combinatorics10.1007/978-3-319-62389-4_49(591-602)Online publication date: 1-Jul-2017
https://doi.org/10.1007/978-3-319-62389-4_49
Avrachenkov KBorkar VSaboo K(2016)Distributed and Asynchronous Methods for Semi-supervised LearningAlgorithms and Models for the Web Graph10.1007/978-3-319-49787-7_4(34-46)Online publication date: 11-Nov-2016
https://doi.org/10.1007/978-3-319-49787-7_4
Acharya SEkbal ASaha SSanthanam PMoreno JDias G(2016)Multi-objective Word Sense Induction Using Content and Interlink ConnectionsNatural Language Processing and Information Systems10.1007/978-3-319-41754-7_36(366-375)Online publication date: 17-Jun-2016
https://doi.org/10.1007/978-3-319-41754-7_36
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Abstract

References

Cited By

Index Terms

Recommendations

Text document clustering based on neighbors

Possibilistic fuzzy co-clustering of large document collections

A neighborhood-based approach for clustering of linked document collections

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