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

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 s...

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Bibliographic Details
Published in:Mathematics (Basel) 2024-04, Vol.12 (8), p.1124
Main Authors: Yang, Yuqi, Yu, Shanshan, Pan, Baicheng, Li, Chenglu, Leung, Man-Fai
Format: Article
Language:English
Subjects:
Algorithms
community detection
Computational linguistics
Decomposition
Factorization
Feature extraction
Language processing
Methods
multiplex networks
Multiplexing
Natural language interfaces
Network analysis
orthogonal non-negative matrix tri-factorization
Propagation
Regularization
Social networks
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12081124