Overlapping Community Detection using Clique Proximity and Modularity Maximization

Significant communities within social network contain some leading members. Detection of significant communities within a reasonable time is very difficult. This work proposes a new algorithm for a significant community detection using clique proximity measurement and modularity maximization. This algorithm has initially detected disjoint communities. Then some key nodes are identified within this disjoint community which appears in more than one of these disjoint communities. These nodes are subsequently utilized to detect overlapping communities. Experimental results show that present community detection approach shows better performance in terms of modularity.