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Tripartite community structure in social bookmarking data
Citation key Neubauer2011a0
Author Neubauer, N. and Obermayer, K.
Pages 267-294
Year 2011
DOI 10.1080/13614568.2011.598952
Journal New Review of Hypermedia and Multimedia
Volume 17
Number 3
Abstract Community detection is a branch of network analysis concerned with identifying strongly connected subnetworks. Social bookmarking sites aggregate datasets of often hundreds of millions of triples (document, user, tag), which, when interpreted as edges of a graph, give rise to special networks called 3-partite, 3-uniform hypergraphs. We identify challenges and opportunities of generalizing community detection and in particular modularity optimization to these structures. Two methods for community detection are introduced that preserve the hypergraph's special structure to different degrees. Their performance is compared on synthetic datasets, showing the benefits of structure preservation. Furthermore, a tool for interactive exploration of the community detection results is introduced and applied to examples from real datasets. We find additional evidence for the importance of structure preservation and, more generally, demonstrate how tripartite community detection can help understand the structure of social bookmarking data.
Bibtex Type of Publication Selected:social
Link to original publication Download Bibtex entry

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