J Biomed Inform - Social network analysis of biomedical research collaboration networks in a CTSA institution.

Tópicos

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Resumo

CKGROUND: The popularity of social networks has triggered a number of research efforts on network analyses of research collaborations in the Clinical and Translational Science Award (CTSA) community. Those studies mainly focus on the general understanding of collaboration networks by measuring common network metrics. More fundamental questions about collaborations still remain unanswered such as recognizing "influential" nodes and identifying potential new collaborations that are most rewarding.METHODS: We analyzed biomedical research collaboration networks (RCNs) constructed from a dataset of research grants collected at a CTSA institution (i.e., University of Arkansas for Medical Sciences (UAMS)) in a comprehensive and systematic manner. First, our analysis covers the full spectrum of a RCN study: from network modeling to network characteristics measurement, from key nodes recognition to potential links (collaborations) suggestion. Second, our analysis employs non-conventional model and techniques including a weighted network model for representing collaboration strength, rank aggregation for detecting important nodes, and Random Walk with Restart (RWR) for suggesting new research collaborations.RESULTS: By applying our models and techniques to RCNs at UAMS prior to and after the CTSA, we have gained valuable insights that not only reveal the temporal evolution of the network dynamics but also assess the effectiveness of the CTSA and its impact on a research institution. We find that collaboration networks at UAMS are not scale-free but small-world. Quantitative measures have been obtained to evident that the RCNs at UAMS are moving towards favoring multidisciplinary research. Moreover, our link prediction model creates the basis of collaboration recommendations with an impressive accuracy (AUC: 0.990, MAP@3: 1.48 and MAP@5: 1.522). Last but not least, an open-source visual analytical tool for RCNs is being developed and released through Github.CONCLUSIONS: Through this study, we have developed a set of techniques and tools for analyzing research collaboration networks and conducted a comprehensive case study focusing on a CTSA institution. Our findings demonstrate the promising future of these techniques and tools in understanding the generative mechanisms of research collaborations and helping identify beneficial collaborations to members in the research community.

Resumo Limpo

ckground popular social network trigger number research effort network analys research collabor clinic translat scienc award ctsa communiti studi main focus general understand collabor network measur common network metric fundament question collabor still remain unansw recogn influenti node identifi potenti new collabor rewardingmethod analyz biomed research collabor network rcns construct dataset research grant collect ctsa institut ie univers arkansa medic scienc uam comprehens systemat manner first analysi cover full spectrum rcn studi network model network characterist measur key node recognit potenti link collabor suggest second analysi employ nonconvent model techniqu includ weight network model repres collabor strength rank aggreg detect import node random walk restart rwr suggest new research collaborationsresult appli model techniqu rcns uam prior ctsa gain valuabl insight reveal tempor evolut network dynam also assess effect ctsa impact research institut find collabor network uam scalefre smallworld quantit measur obtain evid rcns uam move toward favor multidisciplinari research moreov link predict model creat basi collabor recommend impress accuraci auc map map last least opensourc visual analyt tool rcns develop releas githubconclus studi develop set techniqu tool analyz research collabor network conduct comprehens case studi focus ctsa institut find demonstr promis futur techniqu tool understand generat mechan research collabor help identifi benefici collabor member research communiti

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