Overlapping Community Detection Using Non-Negative Matrix Factorization With Orthogonal and Sparseness Constraints

• Published in: IEEE access Vol. 6; pp. 21266 - 21274
• Main Authors: Chen, Naiyue, Liu, Yun, Chao, Han-Chieh
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Clustering algorithms; Community detection; Complex systems; Detection algorithms; Factorization; Matrix decomposition; non-negative matrix factorization; Optimization; orthogonal constraint; ranking optimization; sparse constraint; Sparse matrices; Symmetric matrices
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2783542

Network is an abstract expression of subjects and the relationships among them in the real-world system. Research on community detection can help people understand complex systems and identify network functionality. In this paper, we present a novel approach to community detection that utilizes a nonnegative matrix factorization (NMF) model to divide overlapping community from networks. The study is based on the different physical meanings of the pair of matrices <inline-formula> <tex-math notation="LaTeX">W </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">H </tex-math></inline-formula> to optimize the constraint condition. Many community detection algorithms based on NMF require the number of known communities as a prior condition, which limits the field of application of the algorithms. This paper handled the problem by feature matrix preprocessing and ranking optimization, so that the proposed algorithm can divide the network structure with unknown community number. Experiments demonstrated that the proposed algorithm can effectively divide the community structure, and identify network overlay communities and overlapping nodes.