Analysis of Coauthorship Network in Political Science using Centrality Measures

TL;DR

This paper analyzes a coauthorship network in political science using centrality measures to identify influential authors and cohesive groups, revealing insights into collaboration patterns and domain expertise.

Contribution

It introduces a novel application of multiple centrality measures to analyze coauthorship networks in political science fields, highlighting influential authors and community structures.

Findings

Identified key authors with high centrality scores.

Detected cohesive author groups within the network.

Revealed collaboration patterns and domain expertise.

Abstract

In recent era, networks of data are growing massively and forming a shape of complex structure. Data scientists try to analyze different complex networks and utilize these networks to understand the complex structure of a network in a meaningful way. There is a need to detect and identify such a complex network in order to know how these networks provide communication means while using the complex structure. Social network analysis provides methods to explore and analyze such complex networks using graph theories, network properties and community detection algorithms. In this paper, an analysis of coauthorship network of Public Relation and Public Administration subjects of Microsoft Academic Graph (MAG) is presented, using common centrality measures. The authors belong to different research and academic institutes present all over the world. Cohesive groups of authors have been identified and ranked on the basis of centrality measures, such as betweenness, degree, page rank and closeness. Experimental results show the discovery of authors who are good in specific domain, have a strong field knowledge and maintain collaboration among their peers in the field of Public Relations and Public Administration.