Detecting and analyzing research communities in longitudinal scientific networks

Abstract

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.

Network‐based assessment of collaborative research in neuroscience

“…The descriptive network statistics indicate that productivity within the publication networks increased over time while the density, edge count, and average degree decreased. Note, the density of these networks is similar to other investigations of research communities [3] . The number of connected components within the network decreased from six in 2014 to two in 2016 and four in 2017, suggesting that network is becoming more connected at the macro level.…”

“…SNA has been applied to document productivity and viability of research teams' collaborative interactions over time, including prediction of interdisciplinary collaboration formation [3] , [34] , [35] and cooperative structures and interactions among network members [36] , [37] , [38] . Despite increased SNA investigations into research networks in medical and translational research [3] , [10] , [11] , [34] , [35] , [36] , [39] , [40] , [41] , [42] , [43] , there are few SNA investigations into collaborative research specifically in neuroscience. Thus, there is relatively little information as to how scientists working in translational neuroscience may form collaborative partnerships that are indicative of successful team science.…”

“…In the context of the Center for Neurodegeneration and Translational Neuroscience (CNTN) funded through the National Institute of General Medical Sciences (NIGMS) Centers for Biomedical Research Excellence (COBRE) program, assessment acts to support the development of human capital and research infrastructure necessary for the success of neuroscience research and investigators. The CNTN is reflective of the emerging trend in collaborative, or team, science that has gained ground in biomedical research in part due to the growing evidence that impactful and innovative scientific advances are more likely to result from collaborative science efforts [3–5]. The science of team science, or documenting and evaluating the development and outcomes of collaborative research, has grown into its own robust field, catalyzed by evaluation and assessment policies and recommendations from extramural funding agencies and programs, such as the National Institutes of Health Clinical and Translational Science Awards (CTSA) [6,7].…”

Where are we? Using Scopus to map the literature at the intersection between artificial intelligence and research on crime

“…Given the perfectly fitting nature of networks in capturing relations between entities, network science has, therefore, become a mainstream approach to unfold the characteristics and patterns across scientific domains. For instance, networks have been useful in studying co-authorship in management and organizational studies [1], the structure of regional innovation system research [39], scientific endorsement [14], trends in creativity research [76], the characteristics of the research community and their evolution over time [40]. In light of the success gained by networks in studying how scientists behave and how science occurs, this paper will employ graphs to address the abovementioned aims.…”

Where Are We? Using Scopus to Map the Literature at the Intersection Between Artificial Intelligence and Crime

“…Given the perfectly fitting nature of networks in capturing relations between entities, network science has, therefore, become a mainstream approach to unfold the characteristics and patterns across scientific domains. For instance, networks have been useful in studying co-authorship in management and organizational studies [74], the structure of regional innovation system research [75], scientific endorsement [76], trends in creativity research [77], the characteristics of research community and their evolution over time [78]. In light of the success gained by networks in studying how scientists behave and how science occurs, this paper will employ graphs to address the abovementioned aims.…”