Multiobjective discrete particle swarm optimization for community detection in dynamic networks

Chao Gao, Zhengpeng Chen, Xianghua Li, Zhihong Tian, Shudong Li, Zhen Wang

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

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.

Original languageEnglish
Article number28001
JournalEPL
Volume122
Issue number2
DOIs
StatePublished - Apr 2018

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