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 an overt interdisciplinary research group focused on sensor networks and wireless communication. The popular leading eigenvector community detection algorithm was employed to assign a structural community to each scholar in the network. 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 indicates that, contrary to the common conception of a multi-institutional interdisciplinary research group, collaboration is primarily driven by scholar expertise and physical proximity.
Source: Center for Embedded Network Sensing. Papers. Paper 2223.
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