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Spectral Clustering in Social Networks

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Book cover Advances in Web Mining and Web Usage Analysis (SNAKDD 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5439))

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Abstract

We evaluate various heuristics for hierarchical spectral clustering in large telephone call and Web graphs. Spectral clustering without additional heuristics often produces very uneven cluster sizes or low quality clusters that may consist of several disconnected components, a fact that appears to be common for several data sources but, to our knowledge, no general solution provided so far. Divide-and-Merge, a recently described postfiltering procedure may be used to eliminate bad quality branches in a binary tree hierarchy. We propose an alternate solution that enables k-way cuts in each step by immediately filtering unbalanced or low quality clusters before splitting them further.

Our experiments are performed on graphs with various weight and normalization built based on call detail records and Web crawls. We measure clustering quality both by modularity as well as by the geographic and topical homogeneity of the clusters. Compared to divide-and-merge, we give more homogeneous clusters with a more desirable distribution of the cluster sizes.

Support from a Yahoo Faculty Research Grant and by grant ASTOR NKFP 2/004/05. This work is based on an earlier work: Spectral Clustering in Telephone Call Graphs, in Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, Pages 82–91 (2007) (C) ACM, 2007. http://doi.acm.org/10.1145/1348549.1348559

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Author information

Authors and Affiliations

  1. Data Mining and Web search Research Group, Informatics Laboratory, Computer and Automation Research Institute of the Hungarian Academy of Sciences, Hungary

    Miklós Kurucz, András A. Benczúr, Károly Csalogány & László Lukács

Authors
  1. Miklós Kurucz
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  2. András A. Benczúr
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  3. Károly Csalogány
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  4. László Lukács
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Editor information

Editors and Affiliations

  1. One Microsoft Way, WA 98052, Redmond, USA

    Haizheng Zhang

  2. Faculty of Computer Science, Otto von Guericke University Magdeburg, 39016, Magdeburg, Germany

    Myra Spiliopoulou

  3. School of Computing Science, Telecommunications Information Systems, DePaul University, IL 60604, Chicago, USA

    Bamshad Mobasher

  4. College of Information Science and Technology, Pennsylvania State University, PA 16802, University Park, USA

    C. Lee Giles

  5. Department of Computer Science, University of Massachusetts, MA 01003, Amherst, USA

    Andrew McCallum

  6. Computer Engineering and Computer Science Department, University of Louisville, KY 40292, Louisville, USA

    Olfa Nasraoui

  7. Department of Computer Science and Engineering, University of Minnesota, MN 55455, Minneapolis, USA

    Jaideep Srivastava

  8. College of Information Sciences and Technology, The Pennsylvania State University, PA 16802, Unversity Park, USA

    John Yen

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Kurucz, M., Benczúr, A.A., Csalogány, K., Lukács, L. (2009). Spectral Clustering in Social Networks. In: Zhang, H., et al. Advances in Web Mining and Web Usage Analysis. SNAKDD 2007. Lecture Notes in Computer Science(), vol 5439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00528-2_1

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  • DOI: https://doi.org/10.1007/978-3-642-00528-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00527-5

  • Online ISBN: 978-3-642-00528-2

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