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Shaping the interdisciplinary knowledge network of China: a network analysis based on citation data from 1981 to 2010

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Listed:
  • Chen Liu

    (University of Shanghai for Science and Technology)

  • Wei Shan

    (Beijing University of Aeronautics and Astronautics)

  • Jing Yu

    (East China Normal University)

Abstract

This study builds the interdisciplinary knowledge network of China, which is used to catch the knowledge exchange structure of disciplines, and investigates the evolution process from 1981 to 2010. A network analysis was performed to examine the special structure and we compare state of the networks in different periods to determine how the network has got such properties. The dataset are get from the reference relationship in literature on important Chinese academic journals from 1980 to 2010. The analytical results reveal the hidden network structure of interdisciplinary knowledge flows in China and demonstrate that the network is highly connected and has a homogeneous link structure and heterogeneous weight distribution. Through comparing of the network in three periods, that is 1981–1990, 1991–2000 and 2001–2010, we find that the special evolution process, which is limited by the number of nodes, play an important influence on interdisciplinary knowledge flows.

Suggested Citation

  • Chen Liu & Wei Shan & Jing Yu, 2011. "Shaping the interdisciplinary knowledge network of China: a network analysis based on citation data from 1981 to 2010," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 89-106, October.
    • Handle: RePEc:spr:scient:v:89:y:2011:i:1:d:10.1007_s11192-011-0450-6
    DOI: 10.1007/s11192-011-0450-6
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    Cited by:

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    2. Hyunseok Park & Janghyeok Yoon, 2014. "Assessing coreness and intermediarity of technology sectors using patent co-classification analysis: the case of Korean national R&D," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 853-890, February.
    3. Limei Zhao & Qingpu Zhang & Liang Wang, 2014. "Benefit distribution mechanism in the team members’ scientific research collaboration network," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 363-389, August.
    4. Mao, Jin & Liang, Zhentao & Cao, Yujie & Li, Gang, 2020. "Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes," Journal of Informetrics, Elsevier, vol. 14(4).

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