Clustering Hypergraphs for Discovery of Overlapping Communities in Folksonomies

  • Abhijnan Chakraborty
  • Saptarshi Ghosh
Part of the Modeling and Simulation in Science, Engineering and Technology book series (MSSET)


Some of the most popular sites in the Web today are the social tagging systems or folksonomies (e.g. Delicious, Flickr LastFm) where the users share resources and collaboratively annotate those resources with meaningful tags. This helps in the search and the organization of the vast amount of resources. Folksonomies are modelled as tripartite user-resource-tag hypergraphs to study their network properties. Detecting communities of similar nodes from such networks is a challenging and well-studied problem. However, most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, nodes are often associated with multiple overlapping communities. Users have multiple topical interests, and the same resource is often tagged with semantically different tags. The few attempts to detect overlapping communities work on projections of the hypergraph, which results in significant loss of the information contained in the original tripartite structure. In this chapter, we present “Overlapping Hypergraph Clustering” algorithm which detects overlapping communities in folksonomies using the complete tripartite hypergraph structure. The algorithm converts a hypergraph into its corresponding weighted line graph, using measures of hyperedge similarity. Then simple nonoverlapping communities are detected from the line graph, which in turn produce overlapping communities in the folksonomy. Through extensive experiments on synthetic as well as real folksonomy data, we demonstrate that the “Overlapping Hypergraph Clustering” algorithm can detect better community structures in folksonomies as compared to the existing state-of-the-art algorithms.


Betweenness Centrality Line Graph Community Detection Normalize Mutual Information Multiple Community 
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 Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Microsoft Research IndiaBangaloreIndia
  2. 2.Department of Computer Science and Engineering Indian Institute of Technology KharagpurKharagpurIndia

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