Joint orthogonal symmetric non-negative matrix factorization for community detection in attribute network

Kong, Qingming; Sun, Jianyong; Xu, Zongben

doi:10.1016/j.knosys.2023.111192

Cited by 5 publications

(1 citation statement)

References 77 publications

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“…The algorithm research on community detection in single-layer networks has been improved, and various non-negative matrix decomposition algorithms based on singlelayer networks have been widely applied and achieved good performance [26]. However, in the research of multilayer networks, the non-negative matrix factorization algorithm has not been widely applied.…”

Section: Community Detection With Nmf In Multiplex Networkmentioning

confidence: 99%

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

Yang,

Yu,

Pan

et al. 2024

Mathematics

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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.

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Section: Community Detection With Nmf In Multiplex Networkmentioning

confidence: 99%