Finding Strongly Knit Clusters in Social Networks

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. This paper explores combinatorial properties of internally dense and externally sparse clusters. A simple algorithm is given for provably finding such clusters assuming a sufficiently large gap between internal density and external sparsity. Experiments show that the algorithm is able to identify over 90% of the clusters in real graphs, assuming conditions on external sparsity. Key words: graph clustering, overlapping clusters, social networks, Internet Mathematics 2 November 2009 

• Type: Journal
• Year: 2008
• Where: IM
• Authors: Nina Mishra, Robert Schreiber, Isabelle Stanton, Robert Endre Tarjan
• Keywords: External Sparsity, IM 2007, IM 2008, Internal Density, Social Networks