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

• Type: Article in International Scientific Journal
• Date: 2013

Authors

Liu, DY (Author) Other; Jin, D (Author) Other; Baquero, C (Author) Other; He, DX (Author) Other; Yang, B (Author) Other; Yu, QY (Author) Other

Journal

Title: International Journal Of Computational Intelligence Systems

Vol. 6 No. 5

Pages: 354-369

ISSN: 1875-6891

Publisher: ATLANTIS PRESS

Indexing

ISI Web of Knowledge - 19 Citations

Scopus - 20 Citations

Other information

Authenticus ID: P-002-05W

DOI: 10.1080/18756891.2013.773175

Abstract (EN): 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 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16