Improvisation of cluster efficiency using min-cut algorithm in social networks

Social network is one of the vibrant topic and allow researcher to think in various dimension to provide results with wide scope. In this paper we are aimed at optimizing cluster to enhance the result. It helps to increase the computational performance of a social network as well as maintaining the valid nodes for bidirectional interaction between end users. Proposed methodology applies Girvan Newman clustering algorithm along with minimum cut algorithm for optimization purpose which makes effective interaction with in social communication network. Our research includes two major phases in it. During first phase we apply Girvan Newman algorithm to cluster the nodes with common behavior, properties and characteristics. In the second phase, application of minimum cut algorithm will be done into clustered social network nodes. The major objective is to analyze network nodes and make them stable and active and to remove the nodes that are not interacting with any network nodes that is inactive nodes. Such inactive nodes make the less efficiency and inaccurate communication. For these kind of problem, we are going to apply minimum cut algorithm. It will maximize the effective bidirectional interaction by removing the unwanted, noisy and inconsistent nodes from these clusters.