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Optimal and hierarchical clustering of large-scale hybrid networks for scientific mapping

Author

Listed:
  • Xinhai Liu

    (The People’s Bank of China
    The People’s Bank of China)

  • Wolfgang Glänzel

    (Katholieke Universiteit Leuven
    IRPS)

  • Bart Moor

    (Katholieke Universiteit Leuven)

Abstract

Previous studies have shown that hybrid clustering methods based on textual and citation information outperforms clustering methods that use only one of these components. However, former methods focus on the vector space model. In this paper we apply a hybrid clustering method which is based on the graph model to map the Web of Science database in the mirror of the journals covered by the database. Compared with former hybrid clustering strategies, our method is very fast and even achieves better clustering accuracy. In addition, it detects the number of clusters automatically and provides a top-down hierarchical analysis, which fits in with the practical application. We quantitatively and qualitatively asses the added value of such an integrated analysis and we investigate whether the clustering outcome provides an appropriate representation of the field structure by comparing with a text-only or citation-only clustering and with another hybrid method based on linear combination of distance matrices. Our dataset consists of about 8,000 journals published in the period 2002–2006. The cognitive analysis, including the ranked journals, term annotation and the visualization of cluster structure demonstrates the efficiency of our strategy.

Suggested Citation

  • Xinhai Liu & Wolfgang Glänzel & Bart Moor, 2012. "Optimal and hierarchical clustering of large-scale hybrid networks for scientific mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 473-493, May.
    • Handle: RePEc:spr:scient:v:91:y:2012:i:2:d:10.1007_s11192-011-0600-x
    DOI: 10.1007/s11192-011-0600-x
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    References listed on IDEAS

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    1. Zhang, Lin & Liu, Xinhai & Janssens, Frizo & Liang, Liming & Glänzel, Wolfgang, 2010. "Subject clustering analysis based on ISI category classification," Journal of Informetrics, Elsevier, vol. 4(2), pages 185-193.
    2. Lambiotte, R. & Panzarasa, P., 2009. "Communities, knowledge creation, and information diffusion," Journal of Informetrics, Elsevier, vol. 3(3), pages 180-190.
    3. He, Xiaofeng & Zha, Hongyuan & H.Q. Ding, Chris & D. Simon, Horst, 2002. "Web document clustering using hyperlink structures," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 19-45, November.
    4. Frizo Janssens & Wolfgang Glänzel & Bart Moor, 2008. "A hybrid mapping of information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(3), pages 607-631, June.
    5. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    6. Loet Leydesdorff & Ismael Rafols, 2009. "A global map of science based on the ISI subject categories," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(2), pages 348-362, February.
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    Cited by:

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    3. Mariani, Marcello & Borghi, Matteo, 2019. "Industry 4.0: A bibliometric review of its managerial intellectual structure and potential evolution in the service industries," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    4. Ying Huang & Wolfgang Glänzel & Lin Zhang, 2021. "Tracing the development of mapping knowledge domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6201-6224, July.
    5. Lin Zhang & Beibei Sun & Fei Shu & Ying Huang, 2022. "Comparing paper level classifications across different methods and systems: an investigation of Nature publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7633-7651, December.
    6. Guo Chen & Jing Chen & Yu Shao & Lu Xiao, 2023. "Automatic noise reduction of domain-specific bibliographic datasets using positive-unlabeled learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1187-1204, February.
    7. Xiangfeng Meng & Xinhai Liu & YunHai Tong & Wolfgang Glänzel & Shaohua Tan, 2015. "Multi-view clustering with exemplars for scientific mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1527-1552, December.

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