A generative model of article citation networks of a subject from a large-scale citation database

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.