The aim of this paper is to contribute to the understanding of the structural evolution of scientific collaboration networks. A large body of literature has focused on the structure and evolution of co-authorship networks, typically examining networks within a specific discipline, but spanning different academic organizations. By contrast, this paper narrows its focus to a single academic organization (the University of Florida), but expands the network boundary in two ways: including collaborations among scientists in many different disciplines; and examining three dimensions or layers of scientific collaboration, namely, co-authorship on peer-reviewed scientific articles, co-participation in awarded grants, and co-membership in PhD/Master committees. As a result, collecting data from a five-year time window (2011-2015), we obtain a multiplex longitudinal network including three layers (publications, grants, committees). The geometric intricacies of this network are analyzed by looking at the evolution of its global and local properties, in order to shed light on its stochastic formation process, and on the role played by single investigators. First, we study the network community structure of each layer, and the extent to which community membership is explained by factors such as disciplinary affiliation and workplace location. Results show that intra-department relations are as important as inter-department relations for community formation in the three layers, with department affiliations predicting approximately 50% of the community structure over time. However, we find a high rate of heterogeneity in network communities: publication communities predict respectively 45% and 30% of community memberships in the grant and committee layer. This finding suggests that each dimension of collaboration only partially influences the other, and different mechanisms may drive connectivity in different layers. Second, we test the topological weaknesses of the layers to assess the role of single scholars in connecting different areas of the network. We find that co-authorship and committee network structures are somewhat similar: they appear to gradually converge toward a power-law degree distribution, with a network architecture sustained by interlinked “stars”, which for the co-authorship network is consistent with a small-world model. On the contrary, the grant network shows a core-periphery structure. By testing different breakdown scenarios, we conclude that only the committee layer presents a highly resilient architecture, while network connectivity in the other two layers is strongly dependent on the presence of few hub investigators. This finding has significant implications for academic research policy, suggesting that academic research networks would benefit from a system of incentives for highly-connected scholars to i) remain in the university maintaining an efficient network of collaborations; and ii) increase the involvement of their collaborators in research projects, in order to reduce the dependency of the overall network from their own work. A number of inferential tests and heuristic methodologies are implemented to assess the robustness of our findings
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Access to knowledge is crucial for the development of new and innovative ideas. In the last 20 years, an increasing number of scholars have explored the role of scientific collaborations in connecting a diverse pool of knowledge and in reducing the cost of access to information, as well as their effect on scientific productivity. Along this line of research, in this paper we explore the importance of intellectual interactions among the scientists at the University of Florida, the state’s flagship university, as channeled by their network of publication co-authorship between 2008 and 2014 (approximately 5,000 nodes and 20,000 edges). Assessing the significance and the magnitude of the effect of interactions on academic productivity contributes to the literature on social interactions and scientific networks, and provides policy implications encouraging academic collaborations. An inherent selection effect arises in the formation of co-authorships. We use data on spatial and social proximity of investigators to account for this endogeneity problem. After controlling for endogenous co-authorship formation, unobservable heterogeneity, and time varying factors, we find a positive impact of intellectual collaboration on individual performance, measured in terms of success rate for grant funding.
This article presents the design and implementation of a network intervention to foster scientific collaboration at a research university, and describes an experimental framework for rigorous evaluation of the intervention’s impact. Based on social network analysis of publication and grant data, an innovative type of research funding program was developed as a form of alteration of the university’s collaboration network. The intervention consisted in identifying research communities in the network and creating a new collaborative relation between pairs of unconnected researchers in selected communities. The new collaboration was created to maximally increase the overall cohesion of the target research community. In order to evaluate the impact of the program, we designed a randomized experiment with treatment and control communities based on the Rubin Causal Model approach. The paper describes the intervention design, reports findings from the program implementation, and discusses the statistical framework for future evaluation of the intervention.
A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1) Identify collaborative communities in longitudinal scientific networks, and (2) Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes.
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