Accession Number:

AD1064210

Title:

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

Personal Author(s):

Corporate Author:

University of California - Irvine Irvine United States

Report Date:

2015-07-18

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.

Descriptive Note:

Technical Report

Pages:

0029

Subject Categories:

Communities Of Interest:

Modernization Areas:

Distribution Statement:

Approved For Public Release;

File Size:

0.50MB