A new scalable leader-community detection approach for community detection in social networks

Studying social influence in networks is crucial to understand how behavior spreads. An interesting number of theories were elaborated to analyze how innovations and trends get adopted. The traditional view assumes that a minority of members in a society possess qualities that make them exceptionally persuasive in spreading ideas to others. These exceptional individuals drive trends on behalf of the majority of ordinary people. They are loosely described as being informed, respected, and well connected. The leaders or influential are responsible for the dissemination of information and the propagation of influence. In this paper, we propose a new scalable and a deterministic approach for the detection of communities using leaders nodes named Leader-Community Detection Approach LCDA. The proposed approach has two main steps. The first step is the leaders' retrieval. The second step is the community detection using similarity between nodes. Our algorithms provide good results compared to ground truth membership community.