Work

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

Year: 2015

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1503.04567

Authors: Anandkumar, Anima, Sedghi, Hanie

Institutions: University of California, Irvine, University of Southern California

Language: English

Topic: Tensor decomposition and applications

Subfield: Computational Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Microsoft Research, Office of Naval Research, Army Research Office

Awards: G2747436919, Graphical Approaches to Modeling High-Dimensional Data, G6274897657, G7452299184, G8727049869, G8876996369, CAREER: Modeling Dependencies via Graphs: Scalable Inference Methods for Massive Datasets

OpenAlex ID: W2160832298

Cites: 20

Cited by: 6

Related to: 20