Identification and Implementation of Network Science Models for Centrality Measure using Social Network Analysis

A social structure constituting a set of nodes, representing social actors and edges or lines representing relation between these nodes or actors is a social network. Social network plays a vital role in circulation of information and innovation leading to analysis of the network and attracted attention in research field. The analysis of social network as a whole means, representation of all its actors and structure present in that social network forming part of a community. Community detection aims to divide the network into dense areas of graph. The dense regions usually correlates to entities which are familiar to each other, and form part of a community. Similar tastes and desires of the members in a community, enables exchange of information amongst various communities. Network science is the study of complex networks. There are various models defined in network science. Erdős–Rényi random graph model, Configuration model, Watts–Strogatz small world model, Barabási–Albert (BA) preferential attachment model, Mediation-driven attachment model, Fitness model are the various network science models. The current research work consists of application of these models on real world data and comparison between the final output with the use of R Studio.