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Characterizing Research Leadership Flow Diffusion: Assortative Mixing, Preferential Attachment, Triadic Closure and Reciprocity

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Diversity, Divergence, Dialogue (iConference 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12645))

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Abstract

Research leadership is of great significance to the research collaboration, especially in large scale projections. Characterizing the mechanisms and the processes of research leadership flow diffusion become essential to understand the knowledge flow diffusion in research collaboration. In this paper, we systemically analyze the differences in possibilities research leadership flow occurs between two researchers as seen from the effect of assortative mixing, preferential attachment, triadic closure, and reciprocity via Exponential Random Graph Model (ERGM). We demonstrate that combining both the researchers’ attributes and topological feature effects (assortative mixing, preferential attachment, triadic closure and reciprocity) can better characterize the diffusion of research leadership flow.

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Authors and Affiliations

  1. Wuhan University, Wuhan, 430000, China

    Chaocheng He, Guiyan Ou & Jiang Wu

Authors
  1. Chaocheng He
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  2. Guiyan Ou
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  3. Jiang Wu
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Corresponding author

Correspondence to Chaocheng He .

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Editors and Affiliations

  1. Humboldt University of Berlin, Berlin, Germany

    Katharina Toeppe

  2. Renmin University of China, Beijing, China

    Hui Yan

  3. The University of Hong Kong, Hong Kong, Hong Kong

    Samuel Kai Wah Chu

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He, C., Ou, G., Wu, J. (2021). Characterizing Research Leadership Flow Diffusion: Assortative Mixing, Preferential Attachment, Triadic Closure and Reciprocity. In: Toeppe, K., Yan, H., Chu, S.K.W. (eds) Diversity, Divergence, Dialogue. iConference 2021. Lecture Notes in Computer Science(), vol 12645. Springer, Cham. https://doi.org/10.1007/978-3-030-71292-1_17

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  • DOI: https://doi.org/10.1007/978-3-030-71292-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71291-4

  • Online ISBN: 978-3-030-71292-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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