A joint model for authors characteristics and collaboration pattern in bibliometric networks: a Bayesian approach

Abstract

Demographic and behavioral characteristics of journal authors are important indicators of homophily in co-authorship networks. In the presence of correlations between adjacent nodes (assortative mixing), combining the estimation of the individual characteristics and the network structure results in a well-fitting model, which is capable to provide a deep understanding of the linkage between individual and social properties. This paper aims to propose a novel probabilistic model for the joint distribution of nodal properties (authors' demographic and behavioral characteristics) and network structure (co-authorship connections), based on the nodal similarity effect. A Bayesian approach is used to estimate the model parameters, providing insights about the probabilistic properties of the observed data set. After a detailed analysis of the proposed statistical methodology, we illustrate our approach with an empirical analysis of co-authorship of 1007 journal articles indexed in the ISI Web of Science database in the field of neuroscience between 2009 and 2013.