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Towards understanding longitudinal collaboration networks: a case of mammography performance research

Author

Listed:
  • Seyedamir Tavakoli Taba

    (University of Sydney)

  • Liaquat Hossain

    (University of Sydney
    University of Hong Kong)

  • Simon Reay Atkinson

    (University of Sydney)

  • Sarah Lewis

    (Brain Mind Research Institute, University of Sydney)

Abstract

In this paper, we explore the longitudinal research collaboration network of ‘mammography performance’ over 30 years by creating and analysing a large collaboration network data using Scopus. The study of social networks using longitudinal data may provide new insights into how this collaborative research evolve over time as well as what type of actors influence the whole network in time. The methods and findings presented in this work aim to assist identifying key actors in other research collaboration networks. In doing so, we apply a rank aggregation technique to centrality measures in order to derive a single ranking of influential actors. We argue that there is a strong correlation between the level of degree and closeness centralities of an actor and its influence in the research collaboration network (at macro/country level).

Suggested Citation

  • Seyedamir Tavakoli Taba & Liaquat Hossain & Simon Reay Atkinson & Sarah Lewis, 2015. "Towards understanding longitudinal collaboration networks: a case of mammography performance research," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 531-544, May.
    • Handle: RePEc:spr:scient:v:103:y:2015:i:2:d:10.1007_s11192-015-1560-3
    DOI: 10.1007/s11192-015-1560-3
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    References listed on IDEAS

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    Cited by:

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    2. Xuan Liu & Shan Jiang & Hsinchun Chen & Catherine A. Larson & Mihail C. Roco, 2015. "Modeling knowledge diffusion in scientific innovation networks: an institutional comparison between China and US with illustration for nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1953-1984, December.

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