Volume 122, Number 2, April 2018
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||08 June 2018|
Multiobjective discrete particle swarm optimization for community detection in dynamic networks
1 School of Computer and Information Science, Southwest University - Chongqing 400715, China
2 The Cyberspace Institute of Advanced Technology, Guangzhou University - Guangzhou 510006, China
3 School of Mechanical Engineering and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University - Xian 710072, China
Received: 10 April 2018
Accepted: 16 May 2018
Tracking and identifying the dynamic patterns of evolving communities has recently drawn great attention. How to detect the community structure in a dynamic network has become a popular problem in the field of complex network and evolutionary computing. As a new concept, evolutionary clustering, is proposed to detect the process of dynamic networks under the temporal smoothness framework. Evolutionary-based clustering approaches try to maximize clustering accuracy at the current time step and minimize clustering drift at two successive time steps. But the low accuracy and the pre-setting of parameters limit their effectiveness. In order to overcome these weaknesses, in this paper, the community detection in a dynamic network is transformed into a multiobjective optimization problem. Specifically, we propose a novel decomposition strategy for multiobjective discrete particle swarm optimizationm, which balances the accuracy and the smoothness. The experimental results on synthetic and real-world datasets demonstrate the superiority of the proposed method compared with other state-of-the-art methods.
PACS: 89.75.Fb – Structures and organization in complex systems / 89.75.Kd – Patterns
© EPLA, 2018
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