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Predicting scientific breakthroughs based on knowledge structure variations

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  • Min, Chao
  • Bu, Yi
  • Sun, Jianjun

Abstract

Breakthrough research plays an essential role in the advancement of the scientific system. The identification and recognition of scientific breakthroughs is thus of extreme importance. We propose a citing-structure perspective for observing the unfolding of breakthrough research from variations in knowledge structure. The hypothesis is empirically validated that scientific breakthroughs show distinctive knowledge structure characteristics, which are further utilized to predict breakthroughs in their early stage of formation. These characteristics include average clustering coefficient, average degree, maximum closeness centrality, and maximum eigenvector centrality in the direct citing networks of a breakthrough publication. Several explanations are provided for the effectiveness of the predictive models. We also show that: (1) the number of direct citation counts is of low predictive power, with even a negative impact on prediction performance; (2) disciplinary differences exist in knowledge structure, and this should be taken into account; (3) breakthrough characteristics are most prominent in the first layer of citing networks; (4) timing is critical, and 2- to 3-year-old citing networks have greater predictive power.

Suggested Citation

  • Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    • Handle: RePEc:eee:tefoso:v:164:y:2021:i:c:s0040162520313287
    DOI: 10.1016/j.techfore.2020.120502
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    Cited by:

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    2. Yang, Jinqing & Liu, Zhifeng, 2022. "The effect of citation behaviour on knowledge diffusion and intellectual structure," Journal of Informetrics, Elsevier, vol. 16(1).
    3. Coccia, Mario, 2022. "Probability of discoveries between research fields to explain scientific and technological change," Technology in Society, Elsevier, vol. 68(C).
    4. Shiyun Wang & Yaxue Ma & Jin Mao & Yun Bai & Zhentao Liang & Gang Li, 2023. "Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 150-167, February.
    5. Xian Li & Ronald Rousseau & Liming Liang & Fangjie Xi & Yushuang Lü & Yifan Yuan & Xiaojun Hu, 2022. "Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2105-2122, April.
    6. Tohalino, Jorge A.V. & Amancio, Diego R., 2022. "On predicting research grants productivity via machine learning," Journal of Informetrics, Elsevier, vol. 16(2).
    7. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    8. Houqiang Yu & Yian Liang & Yinghua Xie, 2024. "Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades," Mathematics, MDPI, vol. 12(11), pages 1-18, June.

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