100年来引文分布的变化可以揭示科学社区或学科的演变。相对于大量出版物，未被引用论文或高被引用论文的普遍存在为科学研究的动态提供了重要线索。本文利用1900-2006年期间Web of Science上的2500万篇论文和6亿篇参考文献，提出了一个基于随机选择过程的简单模型来解释近年来的“清晰度”现象及其下降。我们表明，未被引用的论文的比例是1）在给定的一年中发表的文章（竞争论文）和2）随后发表的文章（引用论文）的数量和它们包含的参考文献的数量的函数。使用清晰度作为出发点，我们证明了拉伸指数函数和Tsallis函数的一种形式，以适应完整的引用分布在20世纪的效用。例如，与简单的幂律拟合相反，这两种方法都被证明是有经验基础的，并且足够强大，可以更好地理解总水平上的引用动态。基于这些模型的扩展、我们对清晰度的新理解和我们的大型数据集，我们能够为1960年左右引文实践的重要转变提供明确的定量证据和临时解释，这在20世纪是无与伦比的。我们还提出了一个修订的“引文经典”类别作为一组文章，这是明显有别于其他领域。
英文摘要：
Changes in citation distributions over 100 years can reveal much about the evolution of the scientific communities or disciplines. The prevalence of uncited papers or of highly-cited papers, with respect to the bulk of publications, provides important clues as to the dynamics of scientific research. Using 25 million papers and 600 million references from the Web of Science over the 1900-2006 period, this paper proposes a simple model based on a random selection process to explain the "uncitedness" phenomenon and its decline in recent years. We show that the proportion of uncited papers is a function of 1) the number of articles published in a given year (the competing papers) and 2) the number of articles subsequently published (the citing papers) and the number of references they contain. Using uncitedness as a departure point, we demonstrate the utility of the stretched-exponential function and a form of the Tsallis function to fit complete citation distributions over the 20th century. As opposed to simple power-law fits, for instance, both these approaches are shown to be empirically well-grounded and robust enough to better understand citation dynamics at the aggregate level. Based on an expansion of these models, on our new understanding of uncitedness and on our large dataset, we are able provide clear quantitative evidence and provisional explanations for an important shift in citation practices around 1960, unmatched in the 20th century. We also propose a revision of the "citation classic" category as a set of articles which is clearly distinguishable from the rest of the field.
On (Newcomb-)Benford's law: a tale of two papers and of their disproportionate citations. How citation counts can become biased
Nuclear Research Laboratory, Astrophysical Sciences Division, Bhabha Atomic Research Centre（巴哈原子研究中心天体物理科学部核能研究实验室）；GRAPES（经济与社会物理学应用研究者小组）；The Bucharest University of Economic Studies（布加勒斯特经济大学）；University of Leicester（莱斯特大学）；Universitatea Babeș-Bolyai（巴贝什-博约伊大学）
Temporal network modeling with online and hidden vertices based on the birth and death process
The College of Artificial Intelligence, Southwest University（西南大学人工智能学院）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Humboldt University（洪堡大学）
