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Analysis of the relationships among paper citation and its influencing factors: a Bayesian network-based approach

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Abstract

The broad use of citations as evaluation basis has prompted the academic community to think about the mechanism of citations. In this paper, we propose a Bayesian network-based method for the analysis of the relationships among paper citation and its influencing factors. We investigate the factors that may be related to paper citation, calculate the factor values and determine the factor states. Then we design an amended K2 algorithm for Bayesian network structure learning to handle the situation that no strict sort exists among factors. At last, we use Bayesian network inference to analyse the relationships among paper citation and the influencing factors and present certain interesting findings. We believe the method can provide scholars with new intelligence analysis approach, either for citation analysis or other related issues like talent analysis, research areas analysis, and others.

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Notes

  1. APS. APS Data Sets for Research [EB/OL]. [2022–07-20]. https://journals.aps.org/datasets.

  2. https://pgmpy.org/

  3. https://norsys.com/netica.html

  4. By definition HIM should always be larger than HIF.

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Acknowledgements

The work of this paper is supported by the Chinese Academy of Sciences Literature and Information capacity building project, Youth Innovation Promotion Association of Chinese Academy of Sciences (No. 2019176).

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Authors and Affiliations

  1. Wuhan Library, Chinese Academy of Sciences, Wuhan, 430071, China

    Mingyue Sun, Tingcan Ma, Lewei Zhou & Mingliang Yue

  2. Department of Library, Information and Archives Management, University of Chinese Academy of Sciences, Beijing, 100190, China

    Mingyue Sun, Tingcan Ma & Mingliang Yue

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  1. Mingyue Sun
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  2. Tingcan Ma
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Correspondence to Mingliang Yue.

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Sun, M., Ma, T., Zhou, L. et al. Analysis of the relationships among paper citation and its influencing factors: a Bayesian network-based approach. Scientometrics 128, 3017–3033 (2023). https://doi.org/10.1007/s11192-023-04697-3

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