Prediction and application of article potential citations based on nonlinear citation-forecasting combined model

Kehan Wang, Wenxuan Shi, Junsong Bai, Xiaoping Zhao and Liying Zhang ()
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Kehan Wang: Zhengzhou University
Wenxuan Shi: Zhengzhou University
Junsong Bai: Southern Medical University
Xiaoping Zhao: University of California. Irvine
Liying Zhang: Zhengzhou University

Scientometrics, 2021, vol. 126, issue 8, No 9, 6533-6550

Abstract: 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.

Keywords: Citation prediction; Nonlinear citation-forecasting combined model; Paper ranking; Neural network (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11192-021-04026-6

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