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Exploring the topic hierarchy of digital library research in China using keyword networks: a K-core decomposition approach

Author

Listed:
  • Lu Xiao

    (Nanjing University)

  • Guo Chen

    (Nanjing University of Science and Technology)

  • Jianjun Sun

    (Nanjing University)

  • Shuguang Han

    (University of Pittsburgh)

  • Chengzhi Zhang

    (Nanjing University of Science and Technology)

Abstract

Exploring the topic hierarchy of a research field can help us better recognize its intellectual structure. This paper proposes a new method to automatically discover the topic hierarchy, in which the keyword network is constructed to represent topics and their relations, and then decomposed hierarchically into shells using the K-core decomposition method. Adjacent shells with similar morphology are merged into layers according to their density and clustering coefficient. In the keyword network of the digital library field in China, we discover four different layers. The basic layer contains 17 tightly-interconnected core concepts which form the knowledge base of the field. The middle layer contains 13 mediator concepts which are directly connected to technology concepts in the basic layer, showing the knowledge evolution of the field. The detail layer contains 65 concrete concepts which can be grouped into 13 clusters, indicating the research specializations of the field. The marginal layer contains peripheral or isolated concepts.

Suggested Citation

  • Lu Xiao & Guo Chen & Jianjun Sun & Shuguang Han & Chengzhi Zhang, 2016. "Exploring the topic hierarchy of digital library research in China using keyword networks: a K-core decomposition approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1085-1101, September.
    • Handle: RePEc:spr:scient:v:108:y:2016:i:3:d:10.1007_s11192-016-2051-x
    DOI: 10.1007/s11192-016-2051-x
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    References listed on IDEAS

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    Cited by:

    1. Jianbing Ma & Kexin Yang, 2025. "A three-dimensional framework for quantifying knowledge intersection intensity: from a micro perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(1), pages 367-398, January.
    2. Chen, Guo & Hong, Siqi & Du, Chenxin & Wang, Panting & Yang, Zeyu & Xiao, Lu, 2024. "Comparing semantic representation methods for keyword analysis in bibliometric research," Journal of Informetrics, Elsevier, vol. 18(3).
    3. Tong, Tong & Wang, Wanru & Ye, Fred Y., 2024. "A complement to the novel disruption indicator based on knowledge entities," Journal of Informetrics, Elsevier, vol. 18(2).
    4. Zhichao Ba & Yujie Cao & Jin Mao & Gang Li, 2019. "A hierarchical approach to analyzing knowledge integration between two fields—a case study on medical informatics and computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1455-1486, June.
    5. Jean J. Wang & Sarah X. Shao & Fred Y. Ye, 2021. "Identifying 'seed' papers in sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6001-6011, July.

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