A Bayesian approach for the reliability of scientific co-authorship networks with emphasis on nodes

Abstract

The co-authorship among members of a research group commonly can be represented by a (co-authorship) graph in which nodes represent the researchers that make up of this group and edges represent the connections between two agents (i.e., the co-authorship between these agents). Current study measures the reliability of networks by taking into consideration unreliable nodes (researchers) and perfectly reliable edges (co-authorship between two researchers). A Bayesian approach for the reliability of a network represented by the co-authorship among members of a real research group is proposed, obtaining Bayesian estimates and credibility intervals for the individual components (nodes or researchers) and the network. Weakly informative and non-informative prior distributions are assumed for those components and the posterior summaries are obtained by Monte Carlo-Markov Chain methods. The results show the relevance of an inferential approach for the reliability of scientific co-authorship network. The results also demonstrate that the contribution of each researcher is highly relevant for the maintenance of a research group. In addition, the Bayesian methodology was a feasible and easy computational implementation.