A Multiagent Evolutionary Method for Detecting Communities in Complex Networks

Junzhong Ji*, Lang Jiao, Cuicui Yang, Jiming LIU

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)587-614
Number of pages28
JournalComputational Intelligence
Volume32
Issue number4
DOIs
Publication statusPublished - 1 Nov 2016

Scopus Subject Areas

  • Computational Mathematics
  • Artificial Intelligence

User-Defined Keywords

  • community structure detection
  • complex network
  • multiagent evolutionary method
  • random walk

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