Knowledge extraction in social networks is a needful tool as it touches every aspect of our lives such as politic, socio-economic, scientific, etc. Community detection is one of the objectives of this specific tool used for knowledge extraction in social networks. Many algorithms of knowledge extraction from social networks have been developed these last years. However, many of them are not constant, effective and accurate when facing these social networks with many edges. In this paper, we propose a new approach of community detection in social networks with many links between communities. The proposed approach has two steps. In the first step, the algorithm attempts to determine all communities that the clique percolation algorithm may find. In the second step, the algorithm computes the Eigenvector Centrality method on the output of the first step in order to measure the influence of network nodes and reduce the rate of the unclassified nodes. To assess this new approach, we test it on different types of networks. Relevant communities that have been detected testifies effectiveness and performance of the approach over other community detection algorithms.
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