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Research on the Prediction of Highly Cited Papers Based on PCA-BPNN

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Modeling and Simulation of Social-Behavioral Phenomena in Creative Societies (MSBC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1717))

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Abstract

With the increase in scientific research investment, the number of papers has increased significantly, and the evaluation of the impact of papers has received extensive attention from scholars. The citation frequency is the most convenient and widely used index to measure the academic influence of papers. Still, the citation frequency can only measure the real impact of papers some period of time after those have been published. Therefore, to be able to identify highly cited papers at the early stage of publication, this paper collects data on 1025 academic papers published under the library and information discipline of the Web of Science library in 2007 and then extracts 24 predictive characteristics from three aspects: papers, authors, and journals. On this basis, 7 principal component vectors are constructed by feature screening based on PCA. Also, combined with the BP neural network model, the PCA-BPNN highly-cited paper classification prediction model is constructed and finally compared with the other 5 models. The results show that the PCA-BPNN model built in this paper has better prediction performance and provides an effective model for the prediction of paper influence.

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Authors and Affiliations

  1. School of Economics and Management, Harbin Engineering University, Harbin, 150001, People’s Republic of China

    Tian Yu & Changxu Duan

Authors
  1. Tian Yu
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  2. Changxu Duan
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Correspondence to Tian Yu .

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Editors and Affiliations

  1. University of Arkansas at Little Rock, Little Rock, AR, USA

    Nitin Agarwal

  2. Central Economic Mathematical Institute of RAS, Moscow, Russia

    George B. Kleiner

  3. Vilnius Gediminas Technical University, Vilnius, Lithuania

    Leonidas Sakalauskas

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Yu, T., Duan, C. (2023). Research on the Prediction of Highly Cited Papers Based on PCA-BPNN. In: Agarwal, N., Kleiner, G.B., Sakalauskas, L. (eds) Modeling and Simulation of Social-Behavioral Phenomena in Creative Societies. MSBC 2022. Communications in Computer and Information Science, vol 1717. Springer, Cham. https://doi.org/10.1007/978-3-031-33728-4_12

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  • DOI: https://doi.org/10.1007/978-3-031-33728-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33727-7

  • Online ISBN: 978-3-031-33728-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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