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Keyword‑citation‑keyword network: a new perspective of discipline knowledge structure analysis
Qikai Cheng1,2 · Jiamin Wang1,2 · Wei Lu1,2 · Yong Huang1,2 · Yi Bu3
Received: 11 September 2019 / Published online: 25 June 2020 © Akadémiai Kiadó, Budapest, Hungary 2020
Abstract This paper proposes keyword-citation-keyword (KCK) network to analyze the knowledge structure of a discipline. Diferent from traditional co-word network analysis, KCK network highlights the importance of keywords assigned in diferent 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 signifcance. 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.
Keywords: Keyword-citation-keyword network · Co-word network · Knowledge structure · Cluster analysis · Network analysis
Scientometrics (2020) 124:1923–1943
https://doi.org/10.1007/s11192-020-03576-5
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