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