Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization

Xin Luo, Zhigang Liu, Mingsheng Shang, Jungang Lou, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

77 Scopus citations


Community detection, aiming at determining correct affiliation of each node in a network, is a critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) is frequently adopted to handle this task. However, existing SNMF models mostly focus on a network's first-order topological information described by its adjacency matrix without considering the implicit associations among involved nodes. To address this issue, this study proposes a Pointwise mutual information-incorporated and Graph-regularized SNMF (PGS) model. It uses a) Pointwise Mutual Information to quantify implicit associations among nodes, thereby completing the missing but crucial information among critical nodes in a uniform way; b) graph-regularization to achieve precise representation of local topology, and c) SNMF to implement efficient community detection. Empirical studies on eight real-world social networks generated by industrial applications demonstrate that a PGS model achieves significantly higher accuracy gain in community detection than state-of-the-art community detectors.

Original languageEnglish (US)
Article number9271912
Pages (from-to)463-476
Number of pages14
JournalIEEE Transactions on Network Science and Engineering
Issue number1
StatePublished - Jan 1 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications


  • Community Detection
  • Computational Intelligence
  • Graph-regularization
  • Network Representation
  • Pointwise Mutual Information
  • Social Network
  • Symmetric and Non-negative Matrix Factorization


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