School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou City, Jiangxi Province, China

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Abstract—The traditional evolutionary algorithms dividing the complex networks community have some inevitable deficiencies such as low searching accuracy, high computing time complexity, local optimal solution and so on. To address this issue, this paper proposes a novel community structure partition algorithm based on multi-gene families (MGF). First, this algorithm respectively encodes the network entities and the community types into two different multi-gene families according to the MGF’s encoding characteristics in gene expression programming (GEP), and then implicitly encodes the relationship of the two multi-gene families into a chromosome through a mapping function. Meanwhile, the elite migration strategy is applied to the whole genetic stage , that is, gene selection, crossover, inversion, restricted permutation and so on, which could speed up the convergence rate and prevent the premature phenomenon. The study shows that the algorithm proposed is more effective and accurate to solve the community division problem than the traditional evolutionary algorithms.

Index Terms—Complex Networks; Community Structure Division; Multi-gene Families (MGF); Gene Expression Programming (GEP); Elite Migration Strategy

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Cite: Shuzhi Li, Xianmin Wang, "Complex Networks Community Structure Division Algorithm Based on Multi-gene Families Encoding," Journal of Computers vol. 8, no. 11, pp. 3021-3026, 2013.