article

Relevance search and anomaly detection in bipartite graphs

Authors:

Deepayan Chakrabarti,

Christos FaloutsosAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 7, Issue 2

Pages 48 - 55

https://doi.org/10.1145/1117454.1117461

Published: 01 December 2005 Publication History

Abstract

Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (relevance search), and 2) finding nodes connecting irrelevant nodes (anomaly detection). And we propose algorithms to compute the relevance score for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify anomalies, using relevance scores. We evaluate the quality of relevance search based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets.

References

[1]

C. Aggarwal and P. Yu. Outlier detection for high-dimensional data. In SIGMOD, pages 37--46, 2001.

Digital Library

[2]

J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI, 1998.

Digital Library

[3]

Sergey Brin and Lawrence Page. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1--7):107--117, 1998.

Digital Library

[4]

Deepayan Chakrabarti. Autopart: Parameter-free graph partitioning and outlier detection. In PKDD, pages 112--124, 2004.

Digital Library

[5]

Deepayan Chakrabarti, Spiros PapADimitriou, Dharmendra S. Modha, and Christos Faloutsos. Fully automatic cross-associations. In KDD, pages 79--88. ACM Press, 2004.

Digital Library

[6]

I. S. Dhillon, S. Mallela, and D. S. Modha. Information-theoretic co-clustering. In KDD, 2003.

Digital Library

[7]

Gary William Flake, Steve Lawrence, and C. Lee Giles. Efficient identification of Web communities. In KDD, 2000.

Digital Library

[8]

M. Girvan and M. E. J. Newman. Community structure in social and biological networks. In Proc. Natl. Acad. Sci. USA, volume 99, 2002.

[9]

T. Haveliwala. Topic-sensitive pagerank. In Proceedings of the Eleventh International World Wide Web Conference, 2002.

Digital Library

[10]

Taher H. Haveliwala and Sepandar D. Kamvar. The second eigenvalue of the google matrix. Stanford University Technical Report, 2003.

[11]

Glen Jeh and Jennifer Widom. Simrank: a measure of structural-context similarity. In KDD, 2002.

Digital Library

[12]

R. Kannan, S. Vempala, and A. Vetta. On clusterings -- good, bad and spectral. In FOCS, 2000.

Digital Library

[13]

George Karypis and Vipin Kumar. Multilevel k-way partitioning scheme for irregular graphs. Journal of Parallel and Distributed Computing, 48(1):96--129, 1998.

Digital Library

[14]

Stefan Klink, Michael Ley, Emma Rabbidge, Patrick Reuther, Bernd Walter, and Alexander Weber. Browsing and visualizing digital bibliographic data. In VisSym, pages 237--242, 2004.

Digital Library

[15]

C. C. Noble and D. J. Cook. Graph-based anomaly detection. In KDD, pages 631--636, 2003.

Digital Library

[16]

Jia-Yu Pan, Hyung-Jeong Yang, Pinar Duygulu, and Christos Faloutsos. Automatic multimedia cross-modal correlation discovery, In KDD, 2004.

Digital Library

[17]

Berthier Ribeiro-Neto and Berthier Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley, 1999.

Digital Library

[18]

Upendra Shardanand and Pattie Maes. Social information filtering: Algorithms for automating "word of mouth". In Human Factors in Computing Systems, 1995.

Digital Library

[19]

Gilbert Strang. Introduction to Linear Algebra. Wellesley-Cambridge Press, 3 edition, 1998.

Cited By

Sekgoka CYadavalli VAdetunji O(2022)Privacy-preserving data mining of cross-border financial flowsCogent Engineering10.1080/23311916.2022.20466809:1Online publication date: 15-Mar-2022
https://doi.org/10.1080/23311916.2022.2046680
Angiulli FFassetti FSerrao C(2021)A Stochastic Block Model Based Approach to Detect Outliers in NetworksDatabase and Expert Systems Applications10.1007/978-3-030-86472-9_14(149-154)Online publication date: 31-Aug-2021
https://doi.org/10.1007/978-3-030-86472-9_14
Ayeb SHemery BJeanne FCherrier E(2020)Community Detection for Mobile Money Fraud Detection2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS)10.1109/SNAMS52053.2020.9336578(1-6)Online publication date: 14-Dec-2020
https://doi.org/10.1109/SNAMS52053.2020.9336578
Show More Cited By

Index Terms

Relevance search and anomaly detection in bipartite graphs
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Information retrieval query processing
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

Recommendations

Equistarable bipartite graphs

Recently, Milanič and Trotignon introduced the class of equistarable graphs as graphs without isolated vertices admitting positive weights on the edges such that a subset of edges is of total weight 1 if and only if it forms a maximal star. Based on ...
Bipartite subgraphs of triangle-free subcubic graphs

Suppose G is a graph with n vertices and m edges. Let n^' be the maximum number of vertices in an induced bipartite subgraph of G and let m^' be the maximum number of edges in a spanning bipartite subgraph of G. Then b(G)=m^'/m is called the bipartite ...
Representation characterizations of chordal bipartite graphs

A bipartite graph is chordal bipartite if it does not contain an induced cycle of length at least six. We give three representation characterizations of chordal bipartite graphs. More precisely, we show that a bipartite graph is chordal bipartite if and ...

Comments

Information & Contributors

Information

Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 7, Issue 2

December 2005

152 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1117454

Issue’s Table of Contents

Copyright © 2005 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2005

Published in SIGKDD Volume 7, Issue 2

Check for updates

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

49
Total Citations
View Citations
729
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Sekgoka CYadavalli VAdetunji O(2022)Privacy-preserving data mining of cross-border financial flowsCogent Engineering10.1080/23311916.2022.20466809:1Online publication date: 15-Mar-2022
https://doi.org/10.1080/23311916.2022.2046680
Angiulli FFassetti FSerrao C(2021)A Stochastic Block Model Based Approach to Detect Outliers in NetworksDatabase and Expert Systems Applications10.1007/978-3-030-86472-9_14(149-154)Online publication date: 31-Aug-2021
https://doi.org/10.1007/978-3-030-86472-9_14
Ayeb SHemery BJeanne FCherrier E(2020)Community Detection for Mobile Money Fraud Detection2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS)10.1109/SNAMS52053.2020.9336578(1-6)Online publication date: 14-Dec-2020
https://doi.org/10.1109/SNAMS52053.2020.9336578
Jin WJung JKang U(2019)Supervised and extended restart in random walks for ranking and link prediction in networksPLOS ONE10.1371/journal.pone.021385714:3(e0213857)Online publication date: 20-Mar-2019
https://doi.org/10.1371/journal.pone.0213857
Luo CChen ZTang LShrivastava ALi ZChen HYe JGuo YFarooq F(2018)TINETProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3220003(1890-1899)Online publication date: 19-Jul-2018
https://dl.acm.org/doi/10.1145/3219819.3220003
Bodaghi ATeimourpour B(2018)Automobile Insurance Fraud Detection Using Social Network AnalysisApplications of Data Management and Analysis10.1007/978-3-319-95810-1_2(11-16)Online publication date: 5-Oct-2018
https://doi.org/10.1007/978-3-319-95810-1_2
Roundy KTamersoy ASpertus MHart MKats DDell'Amico MScott R(2017)Smoke DetectorProceedings of the 33rd Annual Computer Security Applications Conference10.1145/3134600.3134645(200-211)Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.1145/3134600.3134645
He XGao MKan MWang D(2017)BiRankIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.261158429:1(57-71)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TKDE.2016.2611584
Das AShen MWang J(2017)Modeling user communities for identifying security risks in an organization2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8258488(4481-4486)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8258488
Aksoy SKolda TPinar A(2017)Measuring and modeling bipartite graphs with community structureJournal of Complex Networks10.1093/comnet/cnx0015:4(581-603)Online publication date: 26-Mar-2017
https://doi.org/10.1093/comnet/cnx001
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents