Detection of Communities and Bridges in Weighted Networks

  • Tanwistha Saha
  • Carlotta Domeniconi
  • Huzefa Rangwala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6871)


Traditional graph-based clustering methods group vertices into non-intersecting clusters under the assumption that each vertex can belong to only a single cluster. On the other hand, recent research on graph-based clustering methods, applied to real world networks (e.g., protein-protein interaction networks and social networks), shows overlapping patterns among the underlying clusters. For example, in social networks, an individual is expected to belong to multiple clusters (or communities), rather than strictly confining himself/herself to just one. As such, overlapping clusters enable better models of real-life phenomena. Soft clustering (e.g., fuzzy c-means) has been used with success for network data as well as non-graph data, when the objects are allowed to belong to multiple clusters with a certain degree of membership. In this paper, we propose a fuzzy clustering based approach for community detection in a weighted graphical representation of social and biological networks, for which the ground truth associated to the nodes is available. We compare our results with a baseline method for both multi-labeled and single-labeled datasets.


Ground Truth Fuzzy Cluster Community Detection Multiple Cluster Bridge Node 
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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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tanwistha Saha
    • 1
  • Carlotta Domeniconi
    • 1
  • Huzefa Rangwala
    • 1
  1. 1.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA

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