An Evolutionary Approach Based on Symmetric Nonnegative Matrix Factorization for Community Detection in Dynamic Networks

作者：Yu Pan, Guyu Hu, Zhisong Pan, Shuaihui Wang, Dongsheng Shao · 发表于：IEICE Transactions on Information and Systems · 年份：2019 · DOI：10.1587/transinf.2019edl8046 · 被引用次数：6 · 研究领域：Complex Network Analysis Techniques、Opinion Dynamics and Social Influence、Human Mobility and Location-Based Analysis

Detecting community structures and analyzing temporal evolution in dynamic networks are challenging tasks to explore the inherent characteristics of the complex networks. In this paper, we propose a semi-supervised evolutionary clustering model based on symmetric nonnegative matrix factorization to detect communities in dynamic networks, named sEC-SNMF. We use the results of community partition at the previous time step as the priori information to modify the current network topology, then smooth-out the evolution of the communities and reduce the impact of noise. Furthermore, we introduce a community transition probability matrix to track and analyze the temporal evolutions. Different from previous algorithms, our approach does not need to know the number of communities in advance and can deal with the situation in which the number of communities and nodes varies over time. Extensive experiments on synthetic datasets demonstrate that the proposed method is competitive and has a superior performance.