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.

在本文中，我们分析了从 Web of Science (WoS) 数据库中获得的特定主题的文章引用网络的结构。具体来说，我们修改了 Caldarelli 等人提出的模型。(Phys Rev Lett 89(25):258702, 2002) 并为文章引用网络开发一个生成模型，在该模型中，文章根据本文中介绍的新定义的“重要性”属性接收引用。由于一篇文章的重要性在数量上是不可测量的，我们考虑使用文章的入度，即一篇感兴趣的文章被其他文章引用的次数，作为描述文章重要性的替代量。我们模拟一些入度分布来估计锥形帕累托分布的参数。