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Modeling knowledge diffusion in the disciplinary citation network based on differential dynamics

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Abstract

Knowledge diffusion based on disciplinary citation resembles disease propagation through actual contact. Inspired by the epidemic spread model, the study classifies disciplines from the viewpoint of knowledge diffusion into five states: knowledge recipient disciplines (S), potential knowledge diffusion disciplines (E), knowledge diffusion disciplines (I), knowledge skeptic disciplines (Z), and knowledge immune disciplines (R). The classifications of disciplines can change from one state to another at a rate of α, β, ω, γ, θ or μ. As a result, evolution rules for knowledge diffusion in the disciplinary citation network are created, and the knowledge diffusion SEIZRS model of differential dynamics in the disciplinary citation of a non-uniform network is formed, followed by a comparative analysis between the SEIZRS model and the classic SIR model. Next, the evolution of knowledge diffusion and the influence of state transition parameters on it are discussed to reveal the dynamic mechanism of knowledge diffusion in the disciplinary citation network. Research has shown that the latent mechanism, skeptical mechanism, and feedback mechanism of knowledge introduced in this study can effectively reveal the dynamic mechanism of knowledge diffusion in the disciplinary citation network. The knowledge diffusion state evolution of disciplines in the disciplinary citation network is affected by both the knowledge diffusion evolution states and the relative citation weight (knowledge contact intensity) of neighboring disciplines. Moreover, changes in state transition parameters have different effects on the evolution of knowledge diffusion.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant No. 71704063) and the Tai Shan Scholar Foundation in Shandong province (2021–2025). The paper is an extended version of the ISSI2021 conference paper (Yue et al., 2021).

Funding

This study was supported by National Natural Science Foundation of China (Grant No. 71704063).

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Authors and Affiliations

  1. School of Medical Information Engineering, Jining Medical University, Rizhao, China

    Zenghui Yue

  2. Business School, Shandong University of Technology, Zibo, China

    Haiyun Xu

  3. School of Foreign Languages, Jining Medical University, Rizhao, China

    Guoting Yuan

  4. Institute of Medical Information, Medical Library, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China

    Yan Qi

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  1. Zenghui Yue
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  2. Haiyun Xu
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  3. Guoting Yuan
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  4. Yan Qi
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Correspondence to Zenghui Yue.

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Yue, Z., Xu, H., Yuan, G. et al. Modeling knowledge diffusion in the disciplinary citation network based on differential dynamics. Scientometrics 127, 7593–7613 (2022). https://doi.org/10.1007/s11192-022-04491-7

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