A survey on centrality measure in co-authorship networks in information science journals

The present study aims to investigate the centrality measure in co-authorshipsocial networks in journals of information science indexed by Thompson Reutersdatabase. The present study was carried out using network analysis. The research population consisted of all information science journals which had the impact factors more than0.6. The results showed that Gelnzen had the highest degree, betweeness, closeness and eigenvector centrality in the Sciencetometrics journal while Nicols had thehighest degree, eigenvector and Beta centrality in information science journal. Thefindings indicated that information science researcher's co-authorship social networkgenerally had a low density and a low centrality measure compared to other scientific fields.