We propose a new citation model which builds on the existing models that explicitly or implicitly include "direct" and "indirect" (learning about a cited paper's existence from references in another paper) citation mechanisms. Our model departs from the usual, unrealistic assumption of uniform probability of direct citation, in which initial differences in citation arise purely randomly. Instead, we demonstrate that a two-mechanism model in which the probability of direct citation is proportional to the number of authors on a paper (team size) is able to reproduce the empirical citation distributions of articles published in the field of astronomy remarkably well, and at different points in time. Interpretation of our model is that the intrinsic citation capacity, and hence the initial visibility of a paper, will be enhanced when more people are intimately familiar with some work, favoring papers from larger teams. While the intrinsic citation capacity cannot depend only on the team size, our model demonstrates that it must be to some degree correlated with it, and distributed in a similar way, i.e., having a power-law tail. Consequently, our team-size model qualitatively explains the existence of a correlation between the number of citations and the number of authors on a paper.

中文翻译：

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走向更现实的引文模型：研究团队规模的关键作用

我们提出了一种新的引用模型，该模型建立在现有模型的基础上，这些模型明确或隐含地包含“直接”和“间接”（从另一篇论文的参考文献中了解被引用论文的存在）引用机制。我们的模型与直接引用的统一概率的通常的、不切实际的假设不同，其中引用的初始差异纯粹是随机出现的。相反，我们证明了直接引用概率与论文作者数量（团队规模）成正比的两种机制模型能够非常好地再现天文学领域发表文章的经验引用分布，并且在不同的时间点。我们模型的解释是内在的引用能力，因此论文的初始可见性，当更多的人非常熟悉某些工作时，会得到增强，更喜欢来自更大团队的论文。虽然内在引用能力不能仅取决于团队规模，但我们的模型表明它必须在一定程度上与其相关，并且以类似的方式分布，即具有幂律尾。因此，我们的团队规模模型定性地解释了论文引用次数与作者人数之间存在相关性。