Eigenvector centrality and its application in research professionals' relationship network

Abstract

The centrality of vertices has been the key issue in social network analysis. Many centrality measures have been presented, such as degree, closeness, between's and eigenvector centrality. But eigenvector centrality is more suited than other centrality measures for finding prominent or key author in research professionals' relationship network. In this paper, we discuss eigenvector centrality and its application based on Network x. In eigenvector centrality first set every node a starting amount of influence then performs power iteration method. In network x the starting amount of influence of each node is 1/len(G). Therefore, we modify the eigenvector centrality algorithm and set the starting amount of influence of each node is the degree centrality of that node because eigenvector centrality is the extension of degree centrality and also implements the eigenvector centrality in weighted network.