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Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis

Author

Listed:
  • Qikai Cheng

    (Wuhan University
    Wuhan University)

  • Jiamin Wang

    (Wuhan University
    Wuhan University)

  • Wei Lu

    (Wuhan University
    Wuhan University)

  • Yong Huang

    (Wuhan University
    Wuhan University)

  • Yi Bu

    (Peking University)

Abstract

This paper proposes keyword-citation-keyword (KCK) network to analyze the knowledge structure of a discipline. Different from traditional co-word network analysis, KCK network highlights the importance of keywords assigned in different articles, as well as the semantic relationship between keywords in various articles. In this study, we select computer science domain as an example to illustrate the proposed method. Meanwhile, the results of network analysis, PageRank analysis, and research topic analysis are compared with those of traditional co-word analysis. A total of 110,360 articles with 164,146 unique keywords and 1,615,030 references collected from ACM digital library have been used for this empirical study. The results demonstrate that KCK network outperforms in detecting indirect links between keywords with higher semantic relationship, identifying important knowledge units, as well as discovering the topics with greater significance. Findings from this study contribute to a new perspective and understanding for elucidating discipline knowledge structures, and provide guidance for applying this method in various disciplines.

Suggested Citation

  • Qikai Cheng & Jiamin Wang & Wei Lu & Yong Huang & Yi Bu, 2020. "Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1923-1943, September.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:3:d:10.1007_s11192-020-03576-5
    DOI: 10.1007/s11192-020-03576-5
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