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

TL;DR

This paper introduces a Bayesian probabilistic model that jointly analyzes authors' demographic traits and their collaboration networks, offering a comprehensive understanding of social and individual factors influencing co-authorship patterns.

Contribution

It presents a novel Bayesian model that jointly estimates authors' characteristics and collaboration structures, capturing homophily and assortative mixing in bibliometric networks.

Findings

Model effectively captures the linkage between author traits and collaboration patterns

Empirical analysis reveals significant homophily in neuroscience co-authorships

Provides a probabilistic framework for understanding social dynamics in scientific collaboration

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.