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)


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.


Social Network High Energy Physic Maximal Clique Cluster Criterion Graph Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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