Extended Clique percolation method to detect overlapping community structure

Community detection in social network is a prominent issue in the study of network system as it helps to understand the structure of a network. A member of a social network can be part of more than one group or community. As a member can be overlapped between more than one group, overlapping community detection technique need to be considered in order to identify the overlapping nodes. Clique percolation method is a community detection algorithm which is mostly used for detecting overlapping communities. The problem with this method is that it does not cover the complete network. Hence some nodes may not be a part of any community irrespective of their connectivity. In this paper a novel approach has been introduced to extend the clique percolation method so that each and every connected node will be part of at least one community. The main strategy is to find the initial communities using clique percolation method and then expanding those communities by adding left out nodes which are not included in any community. Left out nodes are included to initial communities based on their belonging coefficient. Real world networks are used to evaluate the proposed algorithm. Quality of the detected community structure is measured by modularity measure which shows proposed method detects communities of better quality over Clique percolation method.