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

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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).

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Authors and Affiliations

  1. Complex Systems Research Group, School of Civil Engineering, Faculty of Engineering and IT, University of Sydney, Sydney, NSW, 2006, Australia

    Seyedamir Tavakoli Taba, Liaquat Hossain & Simon Reay Atkinson

  2. Division of Information and Technology Studies, Faculty of Education, Runme Shaw Bldg, University of Hong Kong, Pokfulam, Hong Kong

    Liaquat Hossain

  3. Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Brain Mind Research Institute, University of Sydney, Sydney, NSW, 2141, Australia

    Sarah Lewis

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  1. Seyedamir Tavakoli Taba
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  2. Liaquat Hossain
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  3. Simon Reay Atkinson
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  4. Sarah Lewis
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Correspondence to Liaquat Hossain.

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Tavakoli Taba, S., Hossain, L., Reay Atkinson, S. et al. Towards understanding longitudinal collaboration networks: a case of mammography performance research. Scientometrics 103, 531–544 (2015). https://doi.org/10.1007/s11192-015-1560-3

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