A New Brainstorming Based Algorithm for the Community Detection Problem

Social network analysis has attracted a lot of attention, especially the community detection problem, which is seen as a graph clustering problem. In this paper, we propose a metaheuristic inspired from the steps of human brainstorming process for creative problems solving, to the challenge of solving the community detection problem in social networks. The proposed method seeks to maximize the network modularity in order to identify the best partition and the most approximate to the real community structure. Experimental results demonstrate that the proposed algorithm gives competitive results on synthetic and real world networks.

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