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

By Anima Anandkumar, Hanie Sedghi. 2015

11 citations recorded by the source corpus.

Author(s): Anandkumar, Anima; Sedghi, Hanie | 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.

Subfields: Computational Mathematics, Statistical and Nonlinear Physics, Transportation

Citations in the corpus

• Beyond CCA: Moment Matching for Multi-View Models (2016, neutral): “Given that K sources are i.i.d. standard normal random variables, α ∼ N (0 , I K ) , the Gaussian CCA model is given by where the matrices Ψ 1 ∈ R M 1 × M 1 and Ψ 2 ∈ R M 2 × M 2 are positive semi-definite.”