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Interrelations among scientific fields and their relative influences revealed by an input–output analysis

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  • Shen, Zhesi
  • Yang, Liying
  • Pei, Jiansuo
  • Li, Menghui
  • Wu, Chensheng
  • Bao, Jianzhang
  • Wei, Tian
  • Di, Zengru
  • Rousseau, Ronald
  • Wu, Jinshan

Abstract

In this paper, we try to answer two questions about any given scientific discipline: first, how important is each subfield and second, how does a specific subfield influence other subfields? We modify the well-known open-system Leontief Input–Output Analysis in economics into a closed-system analysis focusing on eigenvalues and eigenvectors and the effects of removing one subfield. We apply this method to the subfields of physics. This analysis has yielded some promising results for identifying important subfields (for example the field of statistical physics has large influence while it is not among the largest subfields) and describing their influences on each other (for example the subfield of mechanical control of atoms is not among the largest subfields cited by quantum mechanics, but our analysis suggests that these fields are strongly connected). This method is potentially applicable to more general systems that have input–output relations among their elements.

Suggested Citation

  • Shen, Zhesi & Yang, Liying & Pei, Jiansuo & Li, Menghui & Wu, Chensheng & Bao, Jianzhang & Wei, Tian & Di, Zengru & Rousseau, Ronald & Wu, Jinshan, 2016. "Interrelations among scientific fields and their relative influences revealed by an input–output analysis," Journal of Informetrics, Elsevier, vol. 10(1), pages 82-97.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:1:p:82-97
    DOI: 10.1016/j.joi.2015.11.002
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    References listed on IDEAS

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    1. Umed Temurshoev, 2010. "Identifying Optimal Sector Groupings With The Hypothetical Extraction Method," Journal of Regional Science, Wiley Blackwell, vol. 50(4), pages 872-890, October.
    2. Chen, P. & Xie, H. & Maslov, S. & Redner, S., 2007. "Finding scientific gems with Google’s PageRank algorithm," Journal of Informetrics, Elsevier, vol. 1(1), pages 8-15.
    3. Narin, Francis & Hamilton, Kimberly S. & Olivastro, Dominic, 1997. "The increasing linkage between U.S. technology and public science," Research Policy, Elsevier, vol. 26(3), pages 317-330, October.
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    Cited by:

    1. Cinzia Daraio & Francesco Fabbri & Giulia Gavazzi & Maria Grazia Izzo & Luca Leuzzi & Giammarco Quaglia & Giancarlo Ruocco, 2018. "Assessing the interdependencies between scientific disciplinary profiles," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1785-1803, September.
    2. Niu, Qikai & Zhou, Jianlin & Zeng, An & Fan, Ying & Di, Zengru, 2016. "Which publication is your representative work?," Journal of Informetrics, Elsevier, vol. 10(3), pages 842-853.
    3. Du, Ruijin & Wang, Ya & Dong, Gaogao & Tian, Lixin & Liu, Yixiao & Wang, Minggang & Fang, Guochang, 2017. "A complex network perspective on interrelations and evolution features of international oil trade, 2002–2013," Applied Energy, Elsevier, vol. 196(C), pages 142-151.
    4. Zhenquan Lin & Shanci Hou & Jinshan Wu, 2016. "The correlation between editorial delay and the ratio of highly cited papers in Nature, Science and Physical Review Letters," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1457-1464, June.
    5. Li, Menghui & Yang, Liying & Zhang, Huina & Shen, Zhesi & Wu, Chensheng & Wu, Jinshan, 2017. "Do mathematicians, economists and biomedical scientists trace large topics more strongly than physicists?," Journal of Informetrics, Elsevier, vol. 11(2), pages 598-607.

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