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Dynamic subfield analysis of disciplines: an examination of the trading impact and knowledge diffusion patterns of computer science

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  • Yongjun Zhu

    (Drexel University)

  • Erjia Yan

    (Drexel University)

Abstract

The objective of this research is to examine the dynamic impact and diffusion patterns at the subfield level. Using a 15-year citation data set, this research reveals the characteristics of the subfields of computer science from the aspects of citation characteristics, citation link characteristics, network characteristics, and their dynamics. Through a set of indicators including incoming citations, number of citing areas, cited/citing ratios, self-citations ratios, PageRank, and betweenness centrality, the study finds that subfields such as Computer Science Applications, Software, Artificial Intelligence, and Information Systems possessed higher scientific trading impact. Moreover, it also finds that Human–Computer Interaction, Computational Theory and Mathematics, and Computer Science Applications are among the subfields of computer science that gained the fastest growth in impact. Additionally, Engineering, Mathematics, and Decision Sciences form important knowledge channels with subfields in computer science.

Suggested Citation

  • Yongjun Zhu & Erjia Yan, 2015. "Dynamic subfield analysis of disciplines: an examination of the trading impact and knowledge diffusion patterns of computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 335-359, July.
    • Handle: RePEc:spr:scient:v:104:y:2015:i:1:d:10.1007_s11192-015-1594-6
    DOI: 10.1007/s11192-015-1594-6
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Shiyun & Mao, Jin & Lu, Kun & Cao, Yujie & Li, Gang, 2021. "Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth," Journal of Informetrics, Elsevier, vol. 15(4).
    3. Yongjun Zhu & Erjia Yan & Min Song, 2016. "Understanding the evolving academic landscape of library and information science through faculty hiring data," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1461-1478, September.
    4. Xuan Liu & Shan Jiang & Hsinchun Chen & Catherine A. Larson & Mihail C. Roco, 2015. "Modeling knowledge diffusion in scientific innovation networks: an institutional comparison between China and US with illustration for nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1953-1984, December.
    5. Guijie Zhang & Luning Liu & Fangfang Wei, 2019. "Key nodes mining in the inventor–author knowledge diffusion network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 721-735, March.
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    7. John McLevey & Alexander V. Graham & Reid McIlroy-Young & Pierson Browne & Kathryn S. Plaisance, 2018. "Interdisciplinarity and insularity in the diffusion of knowledge: an analysis of disciplinary boundaries between philosophy of science and the sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 331-349, October.

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