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Community detection in graphs has been extensively studied both in theory andin applications. However, detecting communities in hypergraphs is morechallenging. In this paper, we propose a tensor decomposition approach forguaranteed learning of communities in a special class of hypergraphs modelingsocial tagging systems or folksonomies. A folksonomy is a tripartite 3-uniformhypergraph consisting of (user, tag, resource) hyperedges. We posit aprobabilistic mixed membership community model, and prove that the tensormethod consistently learns the communities under efficient sample complexityand separation requirements.

Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods