Nonnegative Matrix Factorization Based on Node Centrality for Community Detection

Sixing Su, Jiewen Guan, Bilian Chen*, Xin Huang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

6 Citations (Scopus)


Community detection is an important topic in network analysis, and recently many community detection methods have been developed on top of the Nonnegative Matrix Factorization (NMF) technique. Most NMF-based community detection methods only utilize the first-order proximity information in the adjacency matrix, which has some limitations. Besides, many NMF-based community detection methods involve sparse regularizations to promote clearer community memberships. However, in most of these regularizations, different nodes are treated equally, which seems unreasonable. To dismiss the above limitations, this article proposes a community detection method based on node centrality under the framework of NMF. Specifically, we design a new similarity measure which considers the proximity of higher-order neighbors to form a more informative graph regularization mechanism, so as to better refine the detected communities. Besides, we introduce the node centrality and Gini impurity to measure the importance of nodes and sparseness of the community memberships, respectively. Then, we propose a novel sparse regularization mechanism which forces nodes with higher node centrality to have smaller Gini impurity. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over thirteen state-of-the-art methods.

Original languageEnglish
Article number84
Number of pages21
JournalACM Transactions on Knowledge Discovery from Data
Issue number6
Early online date27 Dec 2022
Publication statusPublished - 28 Feb 2023

Scopus Subject Areas

  • Computer Science(all)

User-Defined Keywords

  • Community detection
  • nonnegative matrix factorization
  • optimization
  • similarity measure
  • sparse regularization


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