Genetic Algorithm with a Local Search Strategy for Discovering Communities in Complex Networks
Abstract
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 Q is deduced from each nodes 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.
Keywords
Cite
@article{arxiv.1303.5909,
title = {Genetic Algorithm with a Local Search Strategy for Discovering Communities in Complex Networks},
author = {Dayou Liu and Di Jin and Carlos Baquero and Dongxiao He and Bo Yang and Qiangyuan Yu},
journal= {arXiv preprint arXiv:1303.5909},
year = {2013}
}
Comments
17 pages, 8 figures. arXiv admin note: text overlap with arXiv:1303.4711