Modularity Analysis of a Social Network in a Knowledge Institute

Abstract

As the technology is improving faster than any other time before and the growth of technology internet and the connectivity of people and devices gave the rise of network science and the effort to understand the common properties of all kinds of network. Nowadays the use of social network analysis for businesses and organization becomes increasingly significant where objects and entities are represent as nodes and their relationship is represent as edge. The main task of this thesis is to conduct both ego-centric and socio-centric analysis of social network in knowledge institute. The first part analysis is to understand node level characteristics and their importance hub and spoke characteristics and the network clusters. While in the second part the socio-centric of the social network is discussed including components and network modularity. The dataset used for this research is collected from questionnaires then analyzed and visualized using Gephi (Mathieu Bastian 2009) and Cytoscape (KristinaHanspers 2013) Community detection algorithms are used to reveal the overall structure of network and how the network is organized into communities. This study presents the most important nodes in the network according to network centrality including degree closeness and betweenness centrality. The structure of the network contains eight separate components of the network which is in line with the formal structure of of the knowledge institute under the study. However the study reveals seven modules which represent the non-formal contact among nodes in the network. The result from the analysis of this social network can contribute a further insight and understanding of the dynamics and structure of the network and later on can be used re-structuring the network.