Affiliations: [a] Graduate School, Army Engineering University of PLA, Nanjing, Jiangsu 210000, China | [b] Qinhuangdao Campus, Naval Aeronautical University, Qinhuangdao, Hebei 066200, China | [c] College of Information and Communication, National University of Defense Technology, Xi’an, Shaanxi 710106, China | [d] Command and Control Engineering College, Army Engineering University of PLA, Nanjing, Jiangsu 210000, China
Correspondence:
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Corresponding author: Zhisong Pan, Command and Control Engineering College, Army Engineering University of PLA, Nanjing, Jiangsu 210000, China. E-mail: [email protected].
Abstract: Community structure, a foundational concept in understanding networks, is one of the most important properties of dynamic networks. A large number of dynamic community detection methods proposed are based on the temporal smoothness framework that the abrupt change of clustering within a short period is undesirable. However, how to improve the community detection performance by combining network topology information in a short period is a challenging problem. Additionally, previous efforts on utilizing such properties are insufficient. In this paper, we introduce the geometric structure of a network to represent the temporal smoothness in a short time and propose a novel Dynamic Graph Regularized Symmetric NMF method (DGR-SNMF) to detect the community in dynamic networks. This method combines geometric structure information sufficiently in current detecting process by Symmetric Non-negative Matrix Factorization (SNMF). We also prove the convergence of the iterative update rules by constructing auxiliary functions. Extensive experiments on multiple synthetic networks and two real-world datasets demonstrate that the proposed DGR-SNMF method outperforms the state-of-the-art algorithms on detecting dynamic community.
