Analyzing the Social Networks using Blockmodeling Technique

Abstract

With the advent of the Internet, social networks have grown enormously and Social Network Analysis (SNA) has come up as an important field for research. Social networks are represented as graphs where each node called an actor or vertex in a graph represents an individual person or a group of persons and the fundamental component of SNA is the relationship defined by these linkages among units or nodes in the network. In SNA, statistical analysis of relational data is derived using various social network modeling techniques. One of the well known technique blockmodeling groups vertices into clusters and determine the relations between these clusters using matrices as computational tools. It is grounded on different structural concepts like equivalence and positions which are related to the theoretical concepts of social role and role sets. In this thesis, the intent is to generate social networks of varying size using various network tools and analyze the relationship between participants using blockmodeling techniques. The data, generated in binary form, has been analyzed and visualized with varying cluster sizes.