The study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, despite the recent proliferation of quantitative research methods. Traditional network models normally use graph theory to consider articles as nodes and citations as pairwise relationships between them. In this paper, we propose an alternative evolutionary model based on hypergraph theory in which one hyperedge can have an arbitrary number of nodes, combined with an aging effect to reflect the temporal dynamics of scientific citation behavior. Both theoretical approximate solution and simulation analysis of the model are developed and validated using two benchmark datasets from different disciplines, i.e. publications of the American Physical Society (APS) and the Digital Bibliography & Library Project (DBLP). Further analysis indicates that the attraction of early publications will decay exponentially. Moreover, the experimental results show that the aging effect indeed has a significant influence on the description of collective citation patterns. Shedding light on the complex dynamics driving these mechanisms facilitates the understanding of the laws governing scientific evolution and the quantitative evaluation of scientific outputs.

引用网络的研究是科学界感兴趣的。然而，尽管最近有大量的定量研究方法，但驱动个体引用行为的潜在机制仍未得到很好的理解。传统的网络模型通常使用图论将文章视为节点，将引文视为它们之间的成对关系。在本文中，我们提出了一种基于超图理论的替代进化模型，其中一个超边缘可以具有任意数量的节点，并结合衰老效应来反映科学引用行为的时间动态。使用来自不同学科的两个基准数据集（即美国物理学会的出版物（APS）和数字书目和图书馆计划（DBLP）。进一步的分析表明，早期出版物的吸引力将呈指数下降。此外，实验结果表明，老化效应的确对集体引用模式的描述产生了重大影响。阐明驱动这些机制的复杂动力有助于理解科学发展规律和对科学成果进行定量评估。