  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.
