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

Abstract

Community detection in graphs has been extensively studied both in theory and in applications. However, detecting communities in hypergraphs is more challenging. In this paper, we propose a tensor decomposition approach for guaranteed learning of communities in a special class of hypergraphs modeling social tagging systems or folksonomies. A folksonomy is a tripartite 3-uniform hypergraph consisting of (user, tag, resource) hyperedges. We posit a probabilistic mixed membership community model, and prove that the tensor method consistently learns the communities under efficient sample complexity and separation requirements.

References in corpus (2)

• Hanson-Wright inequality and sub-gaussian concentration
• Random weighted projections, random quadratic forms and random eigenvectors