Abstract
In this paper, we analyze the structure of the article citation network of a particular subject obtained from the Web of Science (WoS) database. In specific, we modify a model proposed in Caldarelli et al. (Phys Rev Lett 89(25):258702, 2002) and develop a generative model for article citation networks in which an article receives citations based on a newly defined property called “importance” introduced in this paper. Since the importance of an article is quantitatively unmeasurable, we consider to use the in-degree of articles, which is the number of citations that an article of interest is cited by other articles, as a surrogate quantity to describe an article’s importance. We simulate some in-degree distributions to estimate the parameters of the tapered Pareto distribution. The generative model shows good performance in the comparison between the generated data and data from the real network, especially the citation network of recent years.
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Acknowledgements
The authors would like to thank Clarivate Analytics to provide access to the raw data of the Web of Science database for research investigations, the URA team of ISM for transforming the data into the neo4j database and providing the neo4j database for analysis in this work, and Ms. Ula Tzu-Ning Kung to provide English editing service in this paper. In addition, the authors would like to thank the two reviewers provided many constructive comments and suggestions to improve the quality of this paper. This project was partly supported by Academia Sinica Grant No. AS-TP-109-M07 and the Ministry of Science and Technology (Taiwan) Grant Nos. 107-2118-M-001-011-MY3, 107-2321-B-001-038, 108-2321-B-001-016, and 109-2321-B-001-013. The third author was partly supported by JSPS KAKENHI Grant Number JP20K11715.
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Appendix
Appendix
See appendix Figs. 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 and 26
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Chang, L.LH., Phoa, F.K.H. & Nakano, J. A generative model of article citation networks of a subject from a large-scale citation database. Scientometrics 126, 7373–7395 (2021). https://doi.org/10.1007/s11192-021-04037-3
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DOI: https://doi.org/10.1007/s11192-021-04037-3