Community Detection via Multihop Nonnegative Matrix Factorization

Jiewen Guan, Bilian Chen*, Xin Huang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)

Abstract

Community detection aims at finding all densely connected communities in a network, which serves as a fundamental graph tool for many applications, such as identification of protein functional modules, image segmentation, social circle discovery, to name a few. Recently, nonnegative matrix factorization (NMF)-based community detection methods have attracted significant attention. However, most existing methods neglect the multihop connectivity patterns in a network, which turn out to be practically useful for community detection. In this article, we first propose a novel community detection method, namely multihop NMF (MHNMF for brevity), which takes into account the multihop connectivity patterns in a network. Subsequently, we derive an efficient algorithm to optimize MHNMF and theoretically analyze its computational complexity and convergence. Experimental results on 12 real-world benchmark networks demonstrate that MHNMF outperforms 12 state-of-the-art community detection methods.

Original languageEnglish
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusE-pub ahead of print - 30 Jan 2023

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Clustering methods
  • Community detection
  • graph clustering
  • multiview clustering
  • Network topology
  • nonnegative matrix factorization (NMF)
  • optimization
  • Optimization
  • Sparse matrices
  • Spread spectrum communication
  • Symmetric matrices
  • Task analysis

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