Jiang Bian1, Mengjun Xie2, Umit Topaloglu3, Teresa Hudson4, Hari Eswaran5, William Hogan6. 1. Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA. Electronic address: jbian@uams.edu. 2. Computer Science, University of Arkansas at Little Rock, Little Rock, AR 72204, USA. Electronic address: mxxie@ualr.edu. 3. Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA. Electronic address: utopaloglu@uams.edu. 4. Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; Central Arkansas Veterans Healthcare System, Little Rock, AR 72205, USA. Electronic address: HudsonTeresaJ@uams.edu. 5. Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; Obstetrics & Gynecology Research, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA. Electronic address: EswaranHari@uams.edu. 6. Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA. Electronic address: wrhogan@uams.edu.
Abstract
BACKGROUND: 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. Published by Elsevier Inc.
BACKGROUND: 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. Published by Elsevier Inc.
Entities:
Keywords:
Clinical and Translational Science Award (CTSA); Influential node; Link prediction; Network analysis; Research collaboration network; Small-world
Authors: Gregorio González-Alcaide; Jinseo Park; Charles Huamaní; Isabel Belinchón; José M Ramos Journal: PLoS One Date: 2015-12-11 Impact factor: 3.240