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Unraveling the dynamics of growth, aging and inflation for citations to scientific articles from specific research fields

Author

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  • Higham, K.W.
  • Governale, M.
  • Jaffe, A.B.
  • Zülicke, U.

Abstract

We analyze the time evolution of citations acquired by articles from journals of the American Physical Society (PRA, PRB, PRC, PRD, PRE and PRL). The observed change over time in the number of papers published in each journal is considered an exogenously caused variation in citability that is accounted for by a normalization. The appropriately inflation-adjusted citation rates are found to be separable into a preferential-attachment-type growth kernel and a purely obsolescence-related (i.e., monotonously decreasing as a function of time since publication) aging function. Variations in the empirically extracted parameters of the growth kernels and aging functions associated with different journals point to research-field-specific characteristics of citation intensity and knowledge flow. Comparison with analogous results for the citation dynamics of technology-disaggregated cohorts of patents provides deeper insight into the basic principles of information propagation as indicated by citing behavior.

Suggested Citation

  • Higham, K.W. & Governale, M. & Jaffe, A.B. & Zülicke, U., 2017. "Unraveling the dynamics of growth, aging and inflation for citations to scientific articles from specific research fields," Journal of Informetrics, Elsevier, vol. 11(4), pages 1190-1200.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:4:p:1190-1200
    DOI: 10.1016/j.joi.2017.10.004
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    Citations

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    Cited by:

    1. Katchanov, Yurij L. & Markova, Yulia V. & Shmatko, Natalia A., 2023. "Uncited papers in the structure of scientific communication," Journal of Informetrics, Elsevier, vol. 17(2).
    2. Higham, Kyle & de Rassenfosse, Gaétan & Jaffe, Adam B., 2021. "Patent Quality: Towards a Systematic Framework for Analysis and Measurement," Research Policy, Elsevier, vol. 50(4).
    3. Chakresh Kumar Singh & Demival Vasques Filho & Shivakumar Jolad & Dion R. J. O’Neale, 2020. "Evolution of interdependent co-authorship and citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 385-404, October.
    4. Yu, Dejian & Pan, Tianxing, 2021. "Tracing the main path of interdisciplinary research considering citation preference: A case from blockchain domain," Journal of Informetrics, Elsevier, vol. 15(2).
    5. Li Hou & Qiang Wu & Yundong Xie, 2022. "Does early publishing in top journals really predict long-term scientific success in the business field?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6083-6107, November.
    6. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    7. Jie Liu & Arnulf Grubler & Tieju Ma & Dieter F. Kogler, 2021. "Identifying the technological knowledge depreciation rate using patent citation data: a case study of the solar photovoltaic industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 93-115, January.

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