The clustering of complex networks has become one of the most fascinating elds in recent years. This issue, including social networks, provides useful in- formation. The information leads to optimal structures in complex networks and is very useful for solving real-world problems. Social network graphs have an important feature that random graphs do not have: community structure. The problem of community detection in social network graphs corresponds to graph partitioning and is an NP-hard problem. Therefore, the application of evolutionary algorithms can be an appropriate solution to this problem. This paper uses the Imperialist Competitive Algorithm(ICA) to identify communities within social network graphs. In addition, a parallel version of the ICA was applied to the problem. To compare the performance of ICA with other evolutionary algorithms, we use the PSO algorithm for the community detection problem. The results conrm that PICA(Parallel ICA) is superior in both cost value and convergence rate.

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