Ranking authors of the weighted co-authorship network: analysis of DB Repec data

In the previous paper [12] we investigated the co-authorship network (Nca) represented bv an unweighted graph: nodes correspond to authors, and two authors are considered connected if they are coauthors of at least one publication. Basic network properties are: existence of the giant component (includes 90% of authors), “small worldness” [24] and a power-law fitting of the distribution of coauthors. In this paper we focus on centrality measures in order to identify key authors on the base of the weighted co-authorship network. Using co-authorship data from the distributed database RcPEc [13] we construct two weighted networks that differ in the way of computing edge weights. Let P (\P| = l) be the set of publications and assume that each publication in P has at least two authors. Let V (\V| = n) be the set of authors of these publications and aij = 1 if i is the author of the publication j. For the network N|? the strength of the collaborative tie (the edge weight) i between the authors i and j is equal to the number of joint papers (T-method): w (i,j) = ^ aik · ajk. k=l For the network Npd the edge weight between the authors i and j depends not only on the number of coauthored papers, but also on the number of other coauthors of these papers (F-method [7]): 1 aik · ajk w (i,j) = nk is the number of authors of the publication k. k=l nk -1 The raw data processing procedure is presented in [12], as a result the number of authors \V\ = 32 434 and the number of coauthored publications \P\ = 91113 For each of the network Nca, Nya, NF four measures of centrality such as degree, closeness, betweenness and eigenvector have been calculated and the tables (tabs. 2 4) containing the names of the authors with the highest ranks are provided. It should be noted that these authors have high h-indcx values (according to Google Scholar search engine or IDEAS ranking system [25] based on all publications of the authors). In order to study the dependence of author ranks on the method of calculating the contributions of authors to publications we calculated Pearson’s correlation coefficients and Spearman’s rank correlation coefficients for the same centrality measures for the networks under consideration. It was shown that regardless of how the edge weights are calculated the same centrality measures have significant correlation with each other. The most significant correlation according to both coefficients is fixed for the betweenness centrality, the least for the eigenvector centrality, which determines the “prestige” of the network actor. To illustrate the studied ways of calculating edge weights and the dependence of node ranks on the method and a node location, we considered the 12-node component of Nca and applied four centrality measures to its weighted representations. We see that the ranks of authors differ depending on the method of edge weights calculating. On the base of node ranks we calculated node weights This work was carried out under state contract with ICMMG SB RAS (0251-2021-0005) and presented new ranks of authors (tab. 10) within any component representation and centrality measure used. It is noted that the high ranked authors are the influential persons with a large number of citations. The purpose of further research is to identify the relationship between key authors and the number of citations of coauthored publications. The question of interest is whether collaborative publications receive more citations than single author publications.