Mutual accessibility for community discovery in social networks

The study of networks is an active area of research due to its capability of modeling many real world complex systems. One such interesting property to investigate in any typical network is the community structure which is the division of networks into groups. Social network gains popularity due to its ease of use, as an application of Web 2.0. Social Network Analysis is the field of research that provides a set of tools and theoretical approaches for holistic exploration of the communication and interaction patterns of social systems. Discovering communities in a social network environment is graph partitioning problem, which subdivides the entire graph into smaller partitions. The partitioned sub graph is named as a community. Graph partitioning is believed as NP — hard problem, due to its complexity to split the number of vertices. None of the existing methods bother about whether one person knows the other through any path in the network. In this research, we propose a new approach, called mutual accessibility, to identify the communities in a social network environment. We proved it by synthetic and real — world datasets. This method facilitates the members to have mutual accessibility among each other.