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Community detection in complex networks using extended compact genetic algorithm

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Abstract

Complex networks are often studied as graphs, and detecting communities in a complex network can be modeled as a seriously nonlinear optimization problem. Soft computing techniques have shown promising results for solving this problem. Extended compact genetic algorithm (ECGA) use statistical learning mechanism to build a probability distribution model of all individuals in a population, and then create new population by sampling individuals according to their probability distribution instead of using traditional crossover and mutation operations. ECGA has distinct advantages in solving nonlinear and variable-coupled optimization problems. This paper attempts to apply ECGA to explore community structure in complex networks. Experimental results based on the GN benchmark networks, the LFR benchmark networks, and six real-world complex networks, show that ECGA is more effective than some other algorithms of community detection.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive comments and suggestions which have led to great improvement on this paper, especially on the experiments including statistical significance test and convergence time evaluation. The authors are also grateful to the editors for checking the spelling and references carefully in this paper. This work is supported by the National Natural Science Foundation of China (No. 61132009, 61271374) and the Beijing Natural Science Foundation (No. 4122068).

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  1. Beijing Key Lab of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Jianwu Li & Yulong Song

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  1. Jianwu Li
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  2. Yulong Song
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Correspondence to Jianwu Li.

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Li, J., Song, Y. Community detection in complex networks using extended compact genetic algorithm. Soft Comput 17, 925–937 (2013). https://doi.org/10.1007/s00500-012-0942-1

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