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

DOI

10.1080/18756891.2013.773175How to use a DOI?

Keywords

Complex network, Community mining, Network clustering, Genetic algorithm, Local search, Modularity

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 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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Dayou Liu
AU  - Di Jin
AU  - Carlos Baquero
AU  - Dongxiao He
AU  - Bo Yang
AU  - Qiangyuan Yu
PY  - 2013
DA  - 2013/03/01
TI  - Genetic Algorithm with a Local Search Strategy for Discovering Communities in Complex Networks
JO  - International Journal of Computational Intelligence Systems
SP  - 354
EP  - 369
VL  - 6
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.773175
DO  - 10.1080/18756891.2013.773175
ID  - Liu2013
ER  -