A Multiagent Evolutionary Method for Detecting Communities in Complex Networks
| Autor: | Lang Jiao, Jiming Liu, Junzhong Ji, Cuicui Yang |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Computer science
business.industry Crossover Community structure 02 engineering and technology Complex network Random walk Computational Mathematics Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Evolutionary programming |
| Zdroj: | Computational Intelligence. 32:587-614 |
| ISSN: | 0824-7935 |
| DOI: | 10.1111/coin.12067 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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