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.
Scopus Subject Areas
- Computational Mathematics
- Artificial Intelligence
- community structure detection
- complex network
- multiagent evolutionary method
- random walk