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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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    1. Son Hoang Nguyen & Gobinda Chowdhury, 2013. "Interpreting the knowledge map of digital library research (1990–2010)," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(6), pages 1235-1258, June.
    2. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    3. Verspagen, Bart & Werker, Claudia, 2004. "Keith Pavitt and the Invisible College of the Economics of Technology and Innovation," Research Policy, Elsevier, vol. 33(9), pages 1419-1431, November.
    4. Pei-Chun Lee & Hsin-Ning Su & Te-Yi Chan, 2010. "Assessment of ontology-based knowledge network formation by Vector-Space Model," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 689-703, December.
    5. Son Hoang Nguyen & Gobinda Chowdhury, 2013. "Interpreting the knowledge map of digital library research (1990–2010)," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(6), pages 1235-1258, June.
    6. Star X. Zhao & Paul L. Zhang & Jiang Li & Alice M. Tan & Fred Y. Ye, 2014. "Abstracting the core subnet of weighted networks based on link strengths," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(5), pages 984-994, May.
    7. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    8. Sangyoon Yi & Jinho Choi, 2012. "The organization of scientific knowledge: the structural characteristics of keyword networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(3), pages 1015-1026, March.
    9. Gao-Yong Liu & Ji-Ming Hu & Hui-Ling Wang, 2012. "A co-word analysis of digital library field in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 203-217, April.
    10. Limei Zhao & Qingpu Zhang, 2011. "Mapping knowledge domains of Chinese digital library research output, 1994–2010," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 51-87, October.
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    Cited by:

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    3. 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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