Communities in graphs and hypergraphs
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Modularity for heterogeneous networks
Proceedings of the 21st ACM conference on Hypertext and hypermedia
A graph-based clustering scheme for identifying related tags in folksonomies
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Discovering Overlapping Groups in Social Media
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Identifying overlapping communities in folksonomies or tripartite hypergraphs
Proceedings of the 20th international conference companion on World wide web
Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)
Proceedings of the fifth ACM conference on Recommender systems
OverCite: finding overlapping communities in citation network
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Folksonomies like Delicious and LastFm are modeled as tripartite (user-resource-tag) hypergraphs for studying their network properties. Detecting communities of similar nodes from such networks is a challenging problem. Most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, 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 information contained in the original tripartite structure. We propose the first algorithm to detect overlapping communities in folksonomies using the complete hypergraph structure. Our algorithm converts a hypergraph into its corresponding 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.