Genetic Algorithm with a Local Search Strategy for Discovering Communities in Complex Networks

• Main Authors: Dayou Liu, Di Jin, Carlos Baquero, Dongxiao He, Bo Yang, Qiangyuan Yu
• Format: Article
• Language: English
• Published: Atlantis Press 2013-04-01
• Series: International Journal of Computational Intelligence Systems
• Subjects: Complex network; Community mining; Network clustering; Genetic algorithm; Local search; Modularity
• Online Access: https://www.atlantis-press.com/article/25868391.pdf

In order to further improve the performance of current genetic algorithms aiming at discovering communities, a local search based genetic algorithm (GALS) is here proposed. The core of GALS is a local search based mutation technique. In order to overcome the drawbacks of traditional mutation methods, the paper develops the concept of marginal gene and then the local monotonicity of modularity function is deduced from each node's local view. Based on these two elements, a new mutation method combined with a local search strategy is presented. GALS has been evaluated on both synthetic benchmarks and several real networks, and compared with some presently competing algorithms. Experimental results show that GALS is highly effective and efficient for discovering community structure.