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A review of scientific impact prediction: tasks, features and methods

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Abstract

With the rapid evolution of scientific research, there are a huge volume of papers published every year and the number of scholars is also growing fast. How to effectively predict the scientific impact has become an important research problem, attracting the attention of researchers in various fields, and it is of great significance in improving research efficiency and assisting in decision-making and scientific evaluation. In this paper, we propose a new framework to perform a systematical survey of scientific impact prediction research. Specifically, we take the four common academic entities into account: papers, scholars, venues and institutions. We reviewed all the prediction tasks reported in the literature in detail; the input features are divided into six groups: paper-related, author-related, venue-related, institution-related, network-related and altmetrics-related. Moreover, we classify the forecasting methods into mathematical statistics-based, traditional machine learning-based, deep learning-based and graph-based, and subdivide each category according to the characteristics. Finally, we discuss open issues and existing challenges, and provide potential research directions.

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Notes

  1. 1 http://www.arnetminer.org/data.

  2. 2 https://journals.aps.org/datasets.

  3. 3 https://kdd.org/kdd-cup/view/kdd-cup-2003/Data.

  4. 4 https://github.com/Lucaweihs/impact-prediction/.

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Funding

This research is partially supported by the National Natural Science Foundation of China (Grant Nos. 62202395 and 62176221), Sichuan Academic Achievement Analysis and Application Research Center (Grant No. XSCG2021-021), Natural Science Foundation of Sichuan Province (Grant No. 2022NSFSC0930), Fundamental Research Funds for the Central Universities (Grant No. 2682022CX067) and Youth Talent Startup Grant of SWJTU-Leeds Joint School.

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  1. Library, Southwest Jiaotong University, Chengdu, China

    Wanjun Xia

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Wanjun Xia, Tianrui Li & Chongshou Li

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  1. Wanjun Xia
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Correspondence to Chongshou Li.

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Xia, W., Li, T. & Li, C. A review of scientific impact prediction: tasks, features and methods. Scientometrics 128, 543–585 (2023). https://doi.org/10.1007/s11192-022-04547-8

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