Community Detection in Multiplex Networks Using Orthogonal Non-Negative Matrix Tri-Factorization Based on Graph Regularization and Diversity

Yuqi Yang, Shanshan Yu, Baicheng Pan, Chenglu Li and Man-Fai Leung ()
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Yuqi Yang: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Shanshan Yu: Training and Basic Education Management Office, Southwest University, Chongqing 400715, China
Baicheng Pan: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Chenglu Li: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Man-Fai Leung: School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK

Mathematics, 2024, vol. 12, issue 8, 1-15

Abstract: In recent years, community detection has received increasing interest. In network analysis, community detection refers to the identification of tightly connected subsets of nodes, which are called “communities” or “groups”, in the network. Non-negative matrix factorization models are often used to solve the problem. Orthogonal non-negative matrix tri-factorization (ONMTF) exhibits significant potential as an approach for community detection within multiplex networks. This paper explores the application of ONMTF in multiplex networks, aiming to detect both shared and exclusive communities simultaneously. The model decomposes each layer within the multiplex network into two low-rank matrices. One matrix corresponds to shared communities across all layers, and the other to unique communities within each layer. Additionally, graph regularization and the diversity of private communities are taken into account in the algorithm. The Hilbert Schmidt Independence Criterion (HSIC) is used to constrain the independence of private communities. The results prove that ONMTF effectively addresses community detection in multiplex networks. It also offers strong interpretability and feature extraction capabilities. Therefore, it is an advanced method for community detection in multiplex networks.

Keywords: community detection; orthogonal non-negative matrix tri-factorization; multiplex networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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