| Some of the most popular sites in the Web today are folksonomies or social tagging sites (e.g. Delicious, Flickr, LastFm) where users share resources and collaboratively annotate resources with tags which help in the search, personalized recommendation and organization of the resources. Folksonomies are modeled as tripartite (user-resource-tag) hypergraphs in order to study their network properties, and detecting communities of similar nodes from such networks is a challenging and well-studied problem. However, almost every existing algorithm for community detection in hypergraphs assign unique communities to nodes, whereas in reality, nodes in folksonomies are associated with multiple overlapping communities - users have multiple topical interests and the same resource is often tagged with semantically different tags. We have proposed an algorithm to detect overlapping communities in folksonomies using link-clustering methodology. Through extensive experiments on synthetic as well as real folksonomy data (Delicious, LastFm, MovieLens), we have demonstrated that the proposed algorithm can detect better community structures as compared to existing algorithms for folksonomies. |
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