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Sciabolazza, V. L., Vacca, R., Okraku, T. K., & McCarty, C. (2017). Detecting and analyzing research communities in longitudinal scientific networks. PLOS ONE, 12(8), e0182516. https://doi.org/10.1371/journal.pone.0182516 [Download]
How do we identify research communities at a university? How do we measure the growth of interdisciplinary research in an academic institution? This paper proposes some answers to these problems by exploring the idea of co-membership of two scientists in the same network communities over time.
We draw on the notion of subgroup co-membership to identify collaborative communities in longitudinal scientific networks, and to evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities.
The first part of this work summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. The second part constructs networks of cross-community collaborations and estimates Exponential Random Graph Models to predict the formation of interdisciplinary ties between different communities.
We use longitudinal data on publication and grant collaborations at the University of Florida in 2013-2015. The results show that similar institutional affiliation, spatial proximity, transitivity effects, and the use of the same research services predict higher degree of interdisciplinary collaboration between research communities. They also demonstrate how this method can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with specific research policies, services or institutes.
The data and code used for this paper are available here.