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Using hybrid methods and ‘core documents’ for the representation of clusters and topics: the astronomy dataset

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  • Wolfgang Glänzel

    (KU Leuven
    Library of the Hungarian Academy of Sciences)

  • Bart Thijs

    (KU Leuven)

Abstract

Based on a dataset on Astronomy and Astrophysics, hybrid cluster analyses have been conducted. In order to obtain an optimum solution and to analyse possible issues resulting from the bibliometric methodologies used, we have systematically studied three models and, within these models, two scenarios each. The hybrid clustering was based on a combination of bibliographic coupling and textual similarities using the Louvain method at two resolution levels. The procedure resulted in three clearly hierarchical structures with six and thirteen, seven and thirteen and finally five and eleven clusters, respectively. These structures are analysed with the help of a concordance table. The statistics reflect a high quality of classification. The results of these three models are presented, discussed and compared with each other. For labelling and interpreting clusters, core documents representing the obtained clusters are used. Furthermore, these core documents help depict the internal structure of the complete network and the clusters. This work has been done as part of the international project ‘Measuring the Diversity of Research’ and in the framework a special workshop on the comparative analysis of algorithms for the identification of topics in science organised in Berlin in August 2014.

Suggested Citation

  • Wolfgang Glänzel & Bart Thijs, 2017. "Using hybrid methods and ‘core documents’ for the representation of clusters and topics: the astronomy dataset," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1071-1087, May.
  • Handle: RePEc:spr:scient:v:111:y:2017:i:2:d:10.1007_s11192-017-2301-6
    DOI: 10.1007/s11192-017-2301-6
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    References listed on IDEAS

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    1. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    2. Bart Thijs & Edgar Schiebel & Wolfgang Glänzel, 2013. "Do second-order similarities provide added-value in a hybrid approach?," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 667-677, September.
    3. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    4. Ahlgren, Per & Colliander, Cristian, 2009. "Document–document similarity approaches and science mapping: Experimental comparison of five approaches," Journal of Informetrics, Elsevier, vol. 3(1), pages 49-63.
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    Citations

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    Cited by:

    1. 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.
    2. Rob Koopman & Shenghui Wang & Andrea Scharnhorst, 2017. "Contextualization of topics: browsing through the universe of bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1119-1139, May.
    3. Matthias Held & Grit Laudel & Jochen Gläser, 2021. "Challenges to the validity of topic reconstruction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4511-4536, May.
    4. Mason Youngblood & David Lahti, 2018. "A bibliometric analysis of the interdisciplinary field of cultural evolution," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-9, December.
    5. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "Hybrid self-optimized clustering model based on citation links and textual features to detect research topics," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    6. Shenghui Wang & Rob Koopman, 2017. "Clustering articles based on semantic similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1017-1031, May.
    7. Shuo Xu & Junwan Liu & Dongsheng Zhai & Xin An & Zheng Wang & Hongshen Pang, 2018. "Overlapping thematic structures extraction with mixed-membership stochastic blockmodel," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 61-84, October.
    8. Theresa Velden & Kevin W. Boyack & Jochen Gläser & Rob Koopman & Andrea Scharnhorst & Shenghui Wang, 2017. "Comparison of topic extraction approaches and their results," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1169-1221, May.
    9. Christian Weismayer & Ilona Pezenka, 2017. "Identifying emerging research fields: a longitudinal latent semantic keyword analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1757-1785, December.
    10. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    11. Guadalupe Palacios-Núñez & Gabriel Vélez-Cuartas & Juan D. Botero, 2018. "Developmental tendencies in the academic field of intellectual property through the identification of invisible colleges," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1561-1574, June.
    12. Ba, Zhichao & Liang, Zhentao, 2021. "A novel approach to measuring science-technology linkage: From the perspective of knowledge network coupling," Journal of Informetrics, Elsevier, vol. 15(3).
    13. Wolfgang Glänzel & Sarah Heeffer & Bart Thijs, 2017. "Lexical analysis of scientific publications for nano-level scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1897-1906, June.
    14. Carlos Olmeda-Gómez & Carlos Romá-Mateo & Maria-Antonia Ovalle-Perandones, 2019. "Overview of trends in global epigenetic research (2009–2017)," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1545-1574, June.
    15. Bart Thijs & Wolfgang Glänzel, 2018. "The contribution of the lexical component in hybrid clustering, the case of four decades of “Scientometrics”," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 21-33, April.

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