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.

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Document Details

Document Type
Technical Report
Publication Date
Jul 18, 2015
Accession Number
AD1064210

Entities

People

  • Anima Anandkumar
  • Hanie Sedghi

Organizations

  • University of California, Irvine

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • California
  • Clustering
  • Communities
  • Data Mining
  • Decomposition
  • Eigenvalues
  • Eigenvectors
  • Machine Learning
  • Models
  • Networks
  • Probabilistic Models
  • Probability
  • Random Variables
  • Social Media

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Graph Algorithms and Convex Optimization.