Joint Nonnegative Matrix Factorization for Community Structures Detection in Signed Networks

2018/07/22 by Chao Yan, Yan, Chao, Huimin Cheng +5
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.1807.08281

openalex publication_date 2018/07/22 · openalex created_date 2018/08/03 · openalex updated_date 2026/07/28

Abstract

Community structures detection in signed network is very important for understanding not only the topology structures of signed networks, but also the functions of them, such as information diffusion, epidemic spreading, etc. In this paper, we develop a joint nonnegative matrix factorization model to detect community structures. In addition, we propose modified partition density to evaluate the quality of community structures. We use it to determine the appropriate number of communities. The effectiveness of our approach is demonstrated based on both synthetic and real-world networks.

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