Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Wang, Shuaihuia; b | Li, Guopengc | Hu, Guyud | Wei, Haoa | Pan, Yua | Pan, Zhisongd; *
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: [*] Corresponding author: Zhisong Pan, Command and Control Engineering College, Army Engineering University of PLA, Nanjing, Jiangsu 210000, China. E-mail: hotpzs@hotmail.com.
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.
Keywords: Community detection, dynamic networks, evolutionary clustering, geometric structure, non-negative matrix factorization
DOI: 10.3233/IDA-184432
Journal: Intelligent Data Analysis, vol. 24, no. 1, pp. 119-139, 2020
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl