A Multiagent Evolutionary Method for Detecting Communities in Complex Networks. (2nd June 2015)

• Record Type: Journal Article
• Authors: Ji, Junzhong; Jiao, Lang; Yang, Cuicui; Liu, Jiming
• Is Part Of: Computational intelligence. Volume 32:Number 4(2016)
• Page Start: 587
• Page End: 614
• Publication Date: 2015-06-02
• Subjects: complex network -- community structure detection -- multiagent evolutionary method -- random walk; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12067
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract : Community structure detection in complex networks contributes greatly to the understanding of complex mechanisms in many fields. In this article, we propose a multiagent evolutionary method for discovering communities in a complex network. The focus of the method lies in the evolutionary process of computational agents in a lattice environment, where each agent corresponds to a candidate solution to the community detection problem. First, the method uses a connection‐based encoding scheme to model an agent and a random‐walk behavior to construct a solution. Next, it applies three evolutionary operators, i.e., competition, crossover, and mutation, to realize information exchange among agents and solution evolution. We tested the performance of our method using synthetic and real‐world networks. The results show its capability in effectively detecting community structures.