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Knowledge evolution in physics research: An analysis of bibliographic coupling networks

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  • Wenyuan Liu
  • Andrea Nanetti
  • Siew Ann Cheong

Abstract

Even as we advance the frontiers of physics knowledge, our understanding of how this knowledge evolves remains at the descriptive levels of Popper and Kuhn. Using the American Physical Society (APS) publications data sets, we ask in this paper how new knowledge is built upon old knowledge. We do so by constructing year-to-year bibliographic coupling networks, and identify in them validated communities that represent different research fields. We then visualize their evolutionary relationships in the form of alluvial diagrams, and show how they remain intact through APS journal splits. Quantitatively, we see that most fields undergo weak Popperian mixing, and it is rare for a field to remain isolated/undergo strong mixing. The sizes of fields obey a simple linear growth with recombination. We can also reliably predict the merging between two fields, but not for the considerably more complex splitting. Finally, we report a case study of two fields that underwent repeated merging and splitting around 1995, and how these Kuhnian events are correlated with breakthroughs on Bose-Einstein condensation (BEC), quantum teleportation, and slow light. This impact showed up quantitatively in the citations of the BEC field as a larger proportion of references from during and shortly after these events.

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  • Wenyuan Liu & Andrea Nanetti & Siew Ann Cheong, 2017. "Knowledge evolution in physics research: An analysis of bibliographic coupling networks," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    • Handle: RePEc:plo:pone00:0184821
    DOI: 10.1371/journal.pone.0184821
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    Cited by:

    1. Nguyen, Ai Linh & Liu, Wenyuan & Khor, Khiam Aik & Nanetti, Andrea & Cheong, Siew Ann, 2020. "The golden eras of graphene science and technology: Bibliographic evidences from journal and patent publications," Journal of Informetrics, Elsevier, vol. 14(4).
    2. Stefan Thurner & Wenyuan Liu & Peter Klimek & Siew Ann Cheong, 2020. "The role of mainstreamness and interdisciplinarity for the relevance of scientific papers," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-14, April.
    3. Song Yanhui & Lei Lixin, 2024. "Inventor bibliographic-patent-coupling analysis and inventor-patent-classification-coupling analysis: a comparative analysis based on NPE," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(2), pages 745-765, February.
    4. Giziński, Stanisław & Kaczyńska, Paulina & Ruczyński, Hubert & Wiśnios, Emilia & Pieliński, Bartosz & Biecek, Przemysław & Sienkiewicz, Julian, 2024. "Big Tech influence over AI research revisited: Memetic analysis of attribution of ideas to affiliation," Journal of Informetrics, Elsevier, vol. 18(4).
    5. Yu, Xiaoyao & Szymanski, Boleslaw K. & Jia, Tao, 2021. "Become a better you: Correlation between the change of research direction and the change of scientific performance," Journal of Informetrics, Elsevier, vol. 15(3).

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