The interest in network analysis of bibliographic data has grown substantially in recent years, yet comprehensive statistical models for examining the complete dynamics of scientific networks based on bibliographic data are generally lacking. Current empirical studies often focus on models restricting analysis either to paper citation networks (paper-by-paper) or author networks (author-by-author). However, such networks encompass not only direct connections between papers, but also indirect relationships between the references of papers connected by a citation link. In this paper, we extend recently developed relational hyperevent models (RHEM) for analyzing scientific networks. We introduce new covariates representing theoretically meaningful and empirically interesting sub-network configurations. The model accommodates testing hypotheses considering: (i) the polyadic nature of scientific publication events, and (ii) the interdependencies between authors and references of current and prior papers. We implement the model using purpose-built, publicly available open-source software, demonstrating its empirical value in an analysis of a large publicly available scientific network dataset. Assessing the relative strength of various effects reveals that both the hyperedge structure of publication events, as well as the interconnection between authors and references significantly improve our understanding and interpretation of collaborative scientific production.

翻译：近年来，文献数据的网络分析研究兴趣显著增长，但基于文献数据全面考察科学网络动态过程的统计模型普遍缺乏。当前实证研究常局限于论文引文网络（论文间）或作者网络（作者间）的分析。然而，此类网络不仅包含论文间的直接关联，还隐含引文链路所连接论文参考文献间的间接关系。本文拓展了近期发展的关系型超事件模型（RHEM）以分析科学网络，引入代表理论意义与实证趣味的子网络配置新协变量。该模型可检验以下假设：（i）科学出版事件的多对多（polyadic）性质，以及（ii）当前与先前论文作者及其参考文献间的相互依赖关系。我们采用专为公开设计且可获取的开源软件实现该模型，并通过分析大规模公开科学网络数据集验证其实证价值。对各类效应相对强度的评估表明：出版事件的超边结构以及作者与参考文献间的互连关系，均显著深化了我们对协作性科学生产的理解与诠释。