ABSTRACT

Social network gains popularity due to its ease of use, as an application of Web 2.0 which facilitates users to communicate, interact and share on the World Wide Web. A Social network is a set of people or organizations or other social entities connected by a set of social relationships, such as friendship, co – working or information exchange. Social network analysis is the study of social networks to understand their structure and behavior. 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. Discovering communities in a social network environment is graph partitioning problem. None of the existing methods discussed about knowing the nodes of the network mutually. Hence, we propose a new approach called “mutual accessibility”, to discover communities in a social network environment. We proved comparative study as results by taking both synthetic dataset and real datasets. There is a significant improvement in terms of accuracy and the number of communities discovered in the results obtained by this method.