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

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.

References in corpus (4)

• Finding community structure in networks using the eigenvectors of matrices
• Statistical Mechanics of Community Detection
• Community Structure in Jazz
• The Network of Collaboration Among Rappers and its Community Structure

Cited by in corpus (6)

• Academic team formation as evolving hypergraphs
• Collaboration in sensor network research: an in-depth longitudinal analysis of assortative mixing patterns
• Is academia becoming more localised? The growth of regional knowledge networks within international research collaboration
• Coauthorship networks: A directed network approach considering the order and number of coauthors
• Static and Dynamic Aspects of Scientific Collaboration Networks
• Scalable Analysis for Large Social Networks: the data-aware mean-field approach