Abstract
As the number of academic articles rapidly increases, a reasonable evaluation method for the articles is highly required in the current academic research. Meanwhile, a faster access to the high-quality academic articles for the researchers is also of critical significance. This paper first improves the AVG model and presents a new Nonlinear Citation-Forecasting Combined Model (NCFCM) based on a neural network to predict the potential increase of citation counts. Then, the NCFCM is used to analyze and rank the academic articles in online databases. The results of NCFCM model are compared to the results from other existing methods. Empirical analysis and comparisons demonstrate that the NCFCM model is of high accuracy and robustness in forecasting potential citation counts and ranking academic articles. Ranking academic articles according to the potentional citation counts can help researchers retrieve the desired articles efficiently in a short time.
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This work was supported by the National Social Science Foundation of China (NO.18BTJ021) and National Training Program of Innovation and Entrepreneurship for Undergraduates (NO.202010459036)
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Wang, K., Shi, W., Bai, J. et al. Prediction and application of article potential citations based on nonlinear citation-forecasting combined model. Scientometrics 126, 6533–6550 (2021). https://doi.org/10.1007/s11192-021-04026-6
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DOI: https://doi.org/10.1007/s11192-021-04026-6