An approach based on the clustering coefficient for the community detection in social networks

The community detection in a social network has been become a key issue to discover the most important organizations in networks. Thenceforth, various approaches are proposed to resolve this inference problem. However, the applicability of these existing methods is trapped by their computational cost. In this paper, we propose a promising approach based on the clustering coefficient and the common neighbors similarity to detect the communities, then, we give the mathematical proof of the used technique. The experiments show the efficiency of our proposed method comparing to the CNM and the WalkTrap methods. Moreover, its computational run-time has a linear complexity.