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Predicting the impact and publication date of individual scientists’ future papers

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Abstract

Predicting the future career of individual scientists is an important yet challenging problem with numerous applications such as recruitment of scientific research positions, promoting outstanding academic staff, and managing scientific grant proposals. Despite that much effort has been devoted to predict scientists’ future performance and success, yet these works focus on the macro future performance of scholars from the perspective of their career ages. A related but different task is to predict the impact and publication date of each future paper. We regard this micro level prediction problem as a dynamic series auto-regression task, and a deep learning method is designed to solve it. The experiments show that our method outperforms the state-of-the-art method in this issue.

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Notes

  1. https://journals.aps.org/datasets.

  2. https://www.tensorflow.org/.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant 71731002. Rui-Jie Wang acknowledges the support from the China Scholarship Council (CSC).

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Authors and Affiliations

  1. Department of Physics, University of Fribourg, Fribourg, 1700, Switzerland

    Yuhao Zhou & Ruijie Wang

  2. School of Systems Science, Beijing Normal University, Beijing, 100875, People’s Republic of China

    An Zeng

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  1. Yuhao Zhou
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Correspondence to An Zeng.

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Zhou, Y., Wang, R. & Zeng, A. Predicting the impact and publication date of individual scientists’ future papers. Scientometrics 127, 1867–1882 (2022). https://doi.org/10.1007/s11192-022-04286-w

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