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

TL;DR

This paper introduces GALS, a genetic algorithm enhanced with a local search mutation strategy for improved community detection in complex networks, demonstrating superior performance on benchmarks and real data.

Contribution

The paper proposes a novel local search based mutation technique for genetic algorithms, improving community detection performance in complex networks.

Findings

GALS outperforms existing algorithms on synthetic benchmarks.

GALS is highly effective in real network community detection.

The local search mutation improves efficiency and accuracy.

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.