On the relationship between the structural and socioacademic communities of a coauthorship network

TL;DR

This study compares structural communities in a coauthorship network with scholars' socioacademic attributes, revealing that coauthorship is mainly influenced by departmental and institutional affiliations.

Contribution

It demonstrates that structural communities in coauthorship networks align closely with academic departments and institutions, using multiple community detection algorithms and statistical analysis.

Findings

Communities correspond to same department

Communities correspond to same institution

Coauthorship driven by departmental and institutional ties

Abstract

This article presents a study that compares detected structural communities in a coauthorship network to the socioacademic characteristics of the scholars that compose the network. The coauthorship network was created from the bibliographic record of a multi-institution, interdisciplinary research group focused on the study of sensor networks and wireless communication. Four different community detection algorithms were employed to assign a structural community to each scholar in the network: leading eigenvector, walktrap, edge betweenness and spinglass. Socioacademic characteristics were gathered from the scholars and include such information as their academic department, academic affiliation, country of origin, and academic position. A Pearson's $\chi^2$ test, with a simulated Monte Carlo, revealed that structural communities best represent groupings of individuals working in the same academic department and at the same institution. A generalization of this result suggests that, even in interdisciplinary, multi-institutional research groups, coauthorship is primarily driven by departmental and institutional affiliation.