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Topic scientific community in science: a combined perspective of scientific collaboration and topics

Author

Listed:
  • Jin Mao

    (Wuhan University)

  • Yujie Cao

    (Wuhan University)

  • Kun Lu

    (University of Oklahoma)

  • Gang Li

    (Wuhan University)

Abstract

Scientific communities are clusters of researchers and play important roles in modern science. Studying different forms of scientific communities that either physically or virtually exist is a feasible way to disclose underlying mechanisms of science. From the perspective of complex networks, topology-based communities and topic-based communities reflect scientific collaboration and topical features of science respectively. However, the two features are not isolated but intertwined in scientific practice. This study proposes an approach to detect Topical Scientific Communities (TSCs) with both topology and topic features by applying machine learning techniques and network theory. As an example, the TSCs of the informetrics field are detected, and then the characteristics of these TSCs are analyzed. It is shown that collaboration patterns on the topic level can be revealed by analyzing the static network structure and dynamics of TSCs. Furthermore, cross-topic collaborations at multiple levels could be investigated through TSCs. In addition, TSCs can effectively organize researchers in terms of productivity. Future work will further explore and generalize characteristics of TSCs, and the applications of TSCs to other tasks of studying science.

Suggested Citation

  • Jin Mao & Yujie Cao & Kun Lu & Gang Li, 2017. "Topic scientific community in science: a combined perspective of scientific collaboration and topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 851-875, August.
    • Handle: RePEc:spr:scient:v:112:y:2017:i:2:d:10.1007_s11192-017-2418-7
    DOI: 10.1007/s11192-017-2418-7
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    References listed on IDEAS

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    Cited by:

    1. Tian, Yunpei & Li, Gang & Mao, Jin, 2023. "Predicting the evolution of scientific communities by interpretable machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).
    2. António Correia & Hugo Paredes & Benjamim Fonseca, 2018. "Scientometric analysis of scientific publications in CSCW," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 31-89, January.
    3. Candelaria Barrios & Esther Flores & M. Ángeles Martínez & Marta Ruiz-Martínez, 2019. "Is there convergence in international research collaboration? An exploration at the country level in the basic and applied science fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 631-659, August.

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