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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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    as
    1. Naoki Shibata & Yuya Kajikawa & Katsumori Matsushima, 2007. "Topological analysis of citation networks to discover the future core articles," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(6), pages 872-882, April.
    2. Hu, Xiaojun & Rousseau, Ronald, 2016. "Scientific influence is not always visible: The phenomenon of under-cited influential publications," Journal of Informetrics, Elsevier, vol. 10(4), pages 1079-1091.
    3. Yong Huang & Yi Bu & Ying Ding & Wei Lu, 2018. "Number versus structure: towards citing cascades," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 2177-2193, December.
    4. Zhang, Yi & Lu, Jie & Liu, Feng & Liu, Qian & Porter, Alan & Chen, Hongshu & Zhang, Guangquan, 2018. "Does deep learning help topic extraction? A kernel k-means clustering method with word embedding," Journal of Informetrics, Elsevier, vol. 12(4), pages 1099-1117.
    5. Suominen, Arho & Peng, Haoshu & Ranaei, Samira, 2019. "Examining the dynamics of an emerging research network using the case of triboelectric nanogenerators," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 820-830.
    6. Winnink, J.J. & Tijssen, Robert J.W. & van Raan, A.F.J., 2019. "Searching for new breakthroughs in science: How effective are computerised detection algorithms?," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 673-686.
    7. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
    8. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
    9. Tibor Braun & Wolfgang Glänzel & András Schubert, 2010. "On Sleeping Beauties, Princes and other tales of citation distributions …," Research Evaluation, Oxford University Press, vol. 19(3), pages 195-202, September.
    10. Russell J. Funk & Jason Owen-Smith, 2017. "A Dynamic Network Measure of Technological Change," Management Science, INFORMS, vol. 63(3), pages 791-817, March.
    11. Chaomei Chen, 2012. "Predictive effects of structural variation on citation counts," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(3), pages 431-449, March.
    12. Yoshiyuki Takeda & Yuya Kajikawa, 2010. "Tracking modularity in citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 783-792, June.
    13. Yaqub, Ohid, 2018. "Serendipity: Towards a taxonomy and a theory," Research Policy, Elsevier, vol. 47(1), pages 169-179.
    14. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    15. Jesper W. Schneider & Rodrigo Costas, 2017. "Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(3), pages 709-723, March.
    16. Holly N. Wolcott & Matthew J. Fouch & Elizabeth R. Hsu & Leo G. DiJoseph & Catherine A. Bernaciak & James G. Corrigan & Duane E. Williams, 2016. "Modeling time-dependent and -independent indicators to facilitate identification of breakthrough research papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 807-817, May.
    17. Bettencourt, Luís M.A. & Kaiser, David I. & Kaur, Jasleen, 2009. "Scientific discovery and topological transitions in collaboration networks," Journal of Informetrics, Elsevier, vol. 3(3), pages 210-221.
    18. Chaomei Chen, 2012. "Predictive effects of structural variation on citation counts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(3), pages 431-449, March.
    19. Lv, Yanhua & Ding, Ying & Song, Min & Duan, Zhiguang, 2018. "Topology-driven trend analysis for drug discovery," Journal of Informetrics, Elsevier, vol. 12(3), pages 893-905.
    20. Ponomarev, Ilya V. & Williams, Duane E. & Hackett, Charles J. & Schnell, Joshua D. & Haak, Laurel L., 2014. "Predicting highly cited papers: A Method for Early Detection of Candidate Breakthroughs," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 49-55.
    21. Small, Henry, 2018. "Characterizing highly cited method and non-method papers using citation contexts: The role of uncertainty," Journal of Informetrics, Elsevier, vol. 12(2), pages 461-480.
    22. Xiaojun Hu & Ronald Rousseau, 2017. "Nobel Prize winners 2016: Igniting or sparking foundational publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 1053-1063, February.
    23. Porter, Alan L. & Chiavetta, Denise & Newman, Nils C., 2020. "Measuring tech emergence: A contest," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    24. Guo, Jianfeng & Pan, Jiaofeng & Guo, Jianxin & Gu, Fu & Kuusisto, Jari, 2019. "Measurement framework for assessing disruptive innovations," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 250-265.
    25. Small, Henry & Tseng, Hung & Patek, Mike, 2017. "Discovering discoveries: Identifying biomedical discoveries using citation contexts," Journal of Informetrics, Elsevier, vol. 11(1), pages 46-62.
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    Cited by:

    1. Coccia, Mario, 2022. "Probability of discoveries between research fields to explain scientific and technological change," Technology in Society, Elsevier, vol. 68(C).
    2. 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.
    3. 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.
    4. Luo, Zhuoran & Lu, Wei & He, Jiangen & Wang, Yuqi, 2022. "Combination of research questions and methods: A new measurement of scientific novelty," Journal of Informetrics, Elsevier, vol. 16(2).
    5. Yang, Jinqing & Liu, Zhifeng, 2022. "The effect of citation behaviour on knowledge diffusion and intellectual structure," Journal of Informetrics, Elsevier, vol. 16(1).
    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).

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