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Measuring contextual partner importance in scientific collaboration networks

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  • Schall, Daniel

Abstract

Scientific collaboration commonly takes place in a global and competitive environment. Coalitions and consortia are formed among universities, companies and research institutes to apply for research grants and to perform joint projects. In such a competitive environment, individual institutes may be strategic partners or competitors. Measures to determine partner importance have practical applications such as comparison and rating of competitors, reputation evaluation or performance evaluation of companies and institutes. Many network-centric metrics exist to measure the important of individuals or companies in social and collaborative networks. Here we present a novel context-based metric to measure the importance of partners in scientific collaboration networks. Well-established graph models such as the notion of hubs and authorities provide the basis for this work and are systematically extended to a flexible, context-aware network importance measure.

Suggested Citation

  • Schall, Daniel, 2013. "Measuring contextual partner importance in scientific collaboration networks," Journal of Informetrics, Elsevier, vol. 7(3), pages 730-736.
    • Handle: RePEc:eee:infome:v:7:y:2013:i:3:p:730-736
    DOI: 10.1016/j.joi.2013.05.003
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    References listed on IDEAS

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    1. Ding, Ying, 2011. "Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks," Journal of Informetrics, Elsevier, vol. 5(1), pages 187-203.
    2. Raf Guns & Yu Xian Liu & Dilruba Mahbuba, 2011. "Q-measures and betweenness centrality in a collaboration network: a case study of the field of informetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(1), pages 133-147, April.
    3. Wagner, Caroline S. & Leydesdorff, Loet, 2005. "Network structure, self-organization, and the growth of international collaboration in science," Research Policy, Elsevier, vol. 34(10), pages 1608-1618, December.
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    1. Hur, Wonchang, 2024. "Entropy, heterogeneity, and their impact on technology progress," Journal of Informetrics, Elsevier, vol. 18(2).

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