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Scientific collaboration patterns vary with scholars’ academic ages

Author

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  • Wei Wang

    (Dalian University of Technology
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province)

  • Shuo Yu

    (Dalian University of Technology
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province)

  • Teshome Megersa Bekele

    (Dalian University of Technology
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province)

  • Xiangjie Kong

    (Dalian University of Technology
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province)

  • Feng Xia

    (Dalian University of Technology
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province)

Abstract

Scientists may encounter many collaborators of different academic ages throughout their careers. Thus, they are required to make essential decisions to commence or end a creative partnership. This process can be influenced by strategic motivations because young scholars are pursuers while senior scholars are normally attractors during new collaborative opportunities. While previous works have mainly focused on cross-sectional collaboration patterns, this work investigates scientific collaboration networks from scholars’ local perspectives based on their academic ages. We aim to harness the power of big scholarly data to investigate scholars’ academic-age-aware collaboration patterns. From more than 621,493 scholars and 2,646,941 collaboration records in Physics and Computer Science, we discover several interesting academic-age-aware behaviors. First, in a given time period, the academic age distribution follows the long-tail distribution, where more than 80% scholars are of young age. Second, with the increasing of academic age, the degree centrality of scholars goes up accordingly, which means that senior scholars tend to have more collaborators. Third, based on the collaboration frequency and distribution between scholars of different academic ages, we observe an obvious homophily phenomenon in scientific collaborations. Fourth, the scientific collaboration triads are mostly consisted with beginning scholars. Furthermore, the differences in collaboration patterns between these two fields in terms of academic age are discussed.

Suggested Citation

  • Wei Wang & Shuo Yu & Teshome Megersa Bekele & Xiangjie Kong & Feng Xia, 2017. "Scientific collaboration patterns vary with scholars’ academic ages," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 329-343, July.
  • Handle: RePEc:spr:scient:v:112:y:2017:i:1:d:10.1007_s11192-017-2388-9
    DOI: 10.1007/s11192-017-2388-9
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    References listed on IDEAS

    as
    1. Xiangjie Kong & Huizhen Jiang & Zhuo Yang & Zhenzhen Xu & Feng Xia & Amr Tolba, 2016. "Exploiting Publication Contents and Collaboration Networks for Collaborator Recommendation," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.
    2. Kamal Badar & Julie M. Hite & Naeem Ashraf, 2015. "Knowledge network centrality, formal rank and research performance: evidence for curvilinear and interaction effects," Post-Print hal-02945454, HAL.
    3. Kamal Badar & Julie M. Hite & Naeem Ashraf, 2015. "Knowledge network centrality, formal rank and research performance: evidence for curvilinear and interaction effects," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1553-1576, December.
    4. Katz, J. Sylvan & Martin, Ben R., 1997. "What is research collaboration?," Research Policy, Elsevier, vol. 26(1), pages 1-18, March.
    5. Borrett, Stuart R. & Moody, James & Edelmann, Achim, 2014. "The rise of Network Ecology: Maps of the topic diversity and scientific collaboration," Ecological Modelling, Elsevier, vol. 293(C), pages 111-127.
    6. Cassidy R. Sugimoto & Thomas J. Sugimoto & Andrew Tsou & Staša Milojević & Vincent Larivière, 2016. "Age stratification and cohort effects in scholarly communication: a study of social sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 997-1016, November.
    7. Ortega, José Luis, 2014. "Influence of co-authorship networks in the research impact: Ego network analyses from Microsoft Academic Search," Journal of Informetrics, Elsevier, vol. 8(3), pages 728-737.
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