Detection of Overlapping Communities in Social Tagging Systems

Some of the most popular sites in the Web today are social tagging systems or folksonomies (e.g. Delicious, Flickr, LastFm etc.) where users share resources and collaboratively annotate resources with tags which help in the search, personalized recommendation and organization of the resources. Folksonomies are modelled 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, most existing algorithm for community detection in folksonomies 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. 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 work, we propose the first algorithm to detect overlapping communities in folksonomies using the complete hypergraph structure. Our algorithm converts a hypergraph into its corresponding weighted line-graph, using measures of hyperedge similarity, whereby any community detection algorithm on unipartite graphs can be used to produce overlapping communities in the folksonomy. Through extensive experiments on synthetic as well as real folksonomy data, we demonstrate that the proposed algorithm can detect better community structures as compared to existing state-of-the-art algorithms for folksonomies.