Clustering Social Networks

  • Nina Mishra
  • Robert Schreiber
  • Isabelle Stanton
  • Robert E. Tarjan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4863)

Abstract

Social networks are ubiquitous. The discovery of close-knit clusters in these networks is of fundamental and practical interest. Existing clustering criteria are limited in that clusters typically do not overlap, all vertices are clustered and/or external sparsity is ignored. We introduce a new criterion that overcomes these limitations by combining internal density with external sparsity in a natural way. An algorithm is given for provably finding the clusters, provided there is a sufficiently large gap between internal density and external sparsity. Experiments on real social networks illustrate the effectiveness of the algorithm.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Nina Mishra
    • 1
    • 4
  • Robert Schreiber
    • 2
  • Isabelle Stanton
    • 1
  • Robert E. Tarjan
    • 2
    • 3
  1. 1.Department of Computer Science, University of Virginia 
  2. 2.HP Labs 
  3. 3.Department of Computer Science, Princeton University 
  4. 4.Search Labs, Microsoft Research 

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