Applying Centrality Measures for Impact Analysis in Coauthorship Network

Abstract

Nowadays social networking is an essential part of everyone’s life to communicate with different people around the globe. Due to improvement in expertise networks are growing rapidly and becoming more complex. Through social networking, we can identify different communities that help us to get information about different people and their work in different fields. In social networks, community detection is one of the hot areas. In this paper, we have analyzed a co-authorship network of political science and ranked the authors on the basis of common centrality measures. Finding reveals that these common centrality measures can be useful indicators for impact analysis.