  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.
