Abstract
For the problem of complex network community detection, propose a new algorithm based on genetic algorithm to solve it. This algorithm sets network modularity function as target function and fitness function, uses matrix encoding to describe individuals, and generates initial population using nodes similarity. The crossover operation is based on the quality of individuals’ genes, in this process, all nodes that weren’t partitioned into any communities make up a new one together, and the nodes that were partitioned into more than one community are placed into the community to which most of their neighbors belong. The mutation operation is non-uniform, which splits the mutation gene into two new genes or fuses it into the others randomly. The experiment proved that this algorithm could effectively detect communities in complex networks.
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Li, Y., Liu, G., Lao, Sy. (2013). Complex Network Community Detection Algorithm Based on Genetic Algorithm. In: Qi, E., Shen, J., Dou, R. (eds) The 19th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37270-4_25
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DOI: https://doi.org/10.1007/978-3-642-37270-4_25
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