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

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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.

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Acknowledgements

This work was partially supported by Major Projects of National Social Science Foundation of China (No. 17ZDA292) and National Natural Science Foundation of China (No.71704137).

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Authors and Affiliations

  1. School of Information Management, Wuhan University, Wuhan, 430072, China

    Qikai Cheng, Jiamin Wang, Wei Lu & Yong Huang

  2. Information Retrieval and Knowledge Mining Laboratory, Wuhan University, Wuhan, 430072, China

    Qikai Cheng, Jiamin Wang, Wei Lu & Yong Huang

  3. Department of Information Management, Peking University, Beijing, 100871, China

    Yi Bu

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  1. Qikai Cheng
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  2. Jiamin Wang
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  3. Wei Lu
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  4. Yong Huang
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  5. Yi Bu
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Correspondence to Jiamin Wang.

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Cheng, Q., Wang, J., Lu, W. et al. Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis. Scientometrics 124, 1923–1943 (2020). https://doi.org/10.1007/s11192-020-03576-5

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