Effective Algorithm for Detecting Community Structure in Complex Networks Based on GA and Clustering

Authors:
Xin Liu;Deyi Li;Shuliang Wang;Zhiwei Tao
Affiliations:
State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China;China Institute of Electronic System Engineering, Beijing, 100039, China;State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China;State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Year:
2007

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Abstract

The study of networked systems has experienced a particular surge of interest in the last decade. One issue that has received a considerable amount of attention is the detection and characterization of community structure in networks, meaning the appearance of densely connected groups of vertices, with only sparser connections between groups. In this paper, we present an approach for the problem of community detection using genetic algorithm (GA) in conjunction with the method of clustering. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes daunting complex real-world systems of scale-free network structure.