Enhanced Clustering Technique for Efficient Identification of Independent Groups in Social Networks

The main aim of this paper is to develop a new approach for identifying independent groups among users communicating in social networks using social media applications at any instant. Grouping of users as independent clusters is of dynamic nature as communication between known and unknown users can happen randomly at any point of time. It is becoming inherent to identify the groups, where the members of the group have strong relationship who communicate frequently and consistently via social media applications. Louvain's algorithm will identify the clusters in the community detection process but keeps the lightweight nodes in the original groups without making them into one group by considering the dependence relations. The concept of Bernstein conditions is enhanced and applied to identify the dependency among the users of social networks by formulating equivalence relations, which adhere to the properties of Reflexivity, Symmetricity and Transitivity. Then, the equivalence classes are identified which denote the individual groups of clusters where the users of one cluster are loosely coupled with the users of any other cluster but tightly coupled among the users of the same group. The strength of relationship among the users within the same and different clusters is identified with respect to the quantum of messages being propagated among the users using Louvain's algorithm and the results of equivalence class approach are compared using the same set of commu-nication sequences to show the relation dependency among the members in various clusters.