Symmetry-constrained Non-negative Matrix Factorization Approach for Highly-Accurate Community Detection

Zhigang Liu, Xin Luo, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A community structure is a fundamental property of complex networks and its detection plays an important role in exploring and understanding such networks. Due to its great interpretability, a symmetric and non-negative matrix factorization (SNMF) model is frequently adopted to perform community detection tasks. However, it adopts a single latent factor (LF) matrix to construct the approximation of a given undirected matrix to ensure its absolute symmetry at the expense of shrinking its solution space. This paper proposes a symmetry-constrained NMF (SCNMF) method, with two-fold ideas: a) modeling the approximate symmetry of an undirected network by introducing an equality-constraint on LF matrices into an NMF framework; and b) using graph-regularization to extract the features regarding the intrinsic geometric structure of a network. Extensively empirical studies on six real-world social networks from industrial applications demonstrate that the proposed SCNMF-based detector achieves higher accuracy for community detection than state-of-the-art models.

Original languageEnglish (US)
Title of host publication2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021
PublisherIEEE Computer Society
Pages1521-1526
Number of pages6
ISBN (Electronic)9781665418737
DOIs
StatePublished - Aug 23 2021
Event17th IEEE International Conference on Automation Science and Engineering, CASE 2021 - Lyon, France
Duration: Aug 23 2021Aug 27 2021

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2021-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Country/TerritoryFrance
CityLyon
Period8/23/218/27/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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