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Link Analysis

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Data Mining and Knowledge Discovery Handbook

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Summary

Link analysis is a collection of techniques that operate on data that can be represented as nodes and links. This chapter surveys a variety of techniques including subgraph matching, finding cliques and K-plexes, maximizing spread of influence, visualization, finding hubs and authorities, and combining with traditional techniques (classification, clustering, etc). It also surveys applications including social network analysis, viral marketing, Internet search, fraud detection, and crime prevention.

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References

  • Chakrabarti S, Dom B, Agrawal R, & Raghavan P. Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies. VLDB Journal: Very Large Data Bases 1998. 7:163 –178.

    Article  Google Scholar 

  • Domingos P & Richardson M. Mining the network value of customers. Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining; 2001 August 26 – 29; San Francisco, CA. ACM Press, 2001.

    Google Scholar 

  • Donoho S. & Lewis S. Understand behavior detection technology: Emerging approaches to dealing with three major consumer protection threats. April 2003.

    Google Scholar 

  • Fu D, Remolina E, & Eilbert J. A CBR approach to asymmetric plan detection. Proceedings of Workshop on Link Analysis for Detecting Complex Behavior; 2003 August 27; Washington, DC.

    Google Scholar 

  • Goldenberg A, Kubica J, & Komerak P. A comparison of statistical and machine learning algorithms on the task of link completion. Proceedings of Workshop on Link Analysis for Detecting Complex Behavior; 2003 August 27. Washington, DC.

    Google Scholar 

  • Hanneman R. Introduction to social network methods. Univ of California, Riverside, 2001.

    Google Scholar 

  • Jensen D. Statistical challenges to inductive inference in linked data. Preliminary papers of the 7th International Workshop on Artificial Intelligence and Statistics; 1999 Jan 4 – 6; Fort Lauderdale. FL.

    Google Scholar 

  • Kempe D, Kleinberg J, & Tardos E. Maximizing the spread of influence through a social network. Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2003 August 24 – 27; Washington, DC. ACM Press, 2003.

    Google Scholar 

  • Kleinberg J. Authoritative sources in a hyperlinked environment. Journal of the ACM, 1999, 46,5:604-632.

    Article  MATH  MathSciNet  Google Scholar 

  • Kubica J, Moore A, Schneider J, & Yang Y. Stochastic link and group detection. Proceedings of The Eighth National Conference on Artificial Intelligence; 2002 July 28 – Aug 1; Edmonton, Alberta, Canada. ACM Press, 2002.

    Google Scholar 

  • Kubica J, Moore A, Cohn D, &Schneider J. cGraph: A fast graph-based method for link analysis and queries. Proceedings of Text-Mining & Link-Analysis Workshop; 2003 August 9; Acapulco, Mexico.

    Google Scholar 

  • Lu Q & Getoor L. Link-based text classification. Proceedings of Text-Mining & Link-Analysis Workshop; 2003 August 9; Acapulco, Mexico.

    Google Scholar 

  • Neville J & Jensen D. Iterative classification in relational data. Proceeding of AAAI-2000 Workshop on Learning Statistical Models from Relational Data; 2000 August 3; Austin, TX. AAAI Press, 2000.

    Google Scholar 

  • Neville J, Adler M, & Jensen D. Clustering relational data using attribute and link information. Proceedings of Text-Mining&Link-AnalysisWorkshop; 2003 August 9; Acapulco, Mexico.

    Google Scholar 

  • Oh H, Myaeng S, & Lee M. A practical hypertext categorization method using links and incrementally available class information. Proceedings of the 23rd ACM International Conference on Research and Development in Information Retrieval (SIGIR-00); 2000 July; Athens, Greece.

    Google Scholar 

  • Page L, Brin S, Motwani R, &Winograd T. The PageRank Citation Ranking: Bringing Order to the Web. Stanford Digital Library Technologies Project. 1998.

    Google Scholar 

  • Richardson M & Domingos P. Mining knowledge-sharing sites for viral marketing. Proceedings of Eighth International Conference on Knowledge Discovery and Data Mining; 2002 July 28 – Aug 1; Edmonton, Alberta, Canada. ACM Press, 2002.

    Google Scholar 

  • Rokach, L., Averbuch, M., and Maimon, O., Information retrieval system for medical narrative reports. Lecture notes in artificial intelligence, 3055. pp. 217-228, Springer-Verlag (2004).

    Google Scholar 

  • Rokach, L., Averbuch, M., and Maimon, O., Information retrieval system for medical narrative reports. Lecture notes in artificial intelligence, 3055. pp. 217-228, Springer-Verlag (2004).

    Google Scholar 

  • Rokach L. and Maimon O., Data mining for improving the quality of manufacturing: A feature set decomposition approach. Journal of Intelligent Manufacturing 17(3): 285299, 2006.

    Article  Google Scholar 

  • Ruspini E, Thomere J, & Wolverton M. Database-editing metrics for pattern matching. SRI Intern, March 2003.

    Google Scholar 

  • Senator T, Goldberg H, Wooton J, Cottini A, Umar A, Klinger C, et al. The FinCEN Artificial Intelligence System: Identifying Potential Money Laundering from Reports of Large Cash Transactions. Proceedings of the 7th Conference on Innovative Applications of AI; 1995 August 21 – 23; Montreal, Quebec, Canada. AAAI Press, 1995.

    Google Scholar 

  • Slattery S & Craven M. Combining statistical and relational methods for learning in hypertext domains. Proceedings of ILP-98, 8th International Conference on Inductive Logic 368 Steve Donoho Programming; 1998 July 22 – 24; Madison, WI. Springer Verlag, 1998.

    Google Scholar 

  • Taskar B, Segal E, & Koller D. Probabilistic clustering in relational data. Proceedings of Seventh International Joint Conference on Artificial Intelligence; 2001 August 4 – 10; Seattle, Washington.

    Google Scholar 

  • Wasserman S & Faust K, Social Network Analysis. Cambridge University Press, 1994.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Mantas, Inc., Los Angeles, California, USA

    Steve Donoho

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  1. Steve Donoho
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Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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Donoho, S. (2009). Link Analysis. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_18

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_18

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

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