A novel approach based on the minimum spanning tree to discover communities in social networks

During the last decade, the social networks have known a huge popularity due to their ease of connecting people. The community detection has been in the center of attention in the analysis of this kind of networks. However, this area is still a very active field of research; the majority of methods involving this problem suffers from the accuracy in the determination of meaningful modules or the computational complexity of the used algorithm. In this paper, our contribution is to propose a new method that reveals the communities in social networks based on an excellent similarity measure and the minimum spanning tree. Our approach guaranties both accuracy and efficiency of the resulted partitions when compared with the existing methods and considering five real world social networks with a very promising computational complexity.