The development of suitable statistical models for the analysis of bibliographic networks has trailed behind the empirical ambitions expressed by recent studies of science of science. Extant research typically restricts the analytical focus to either paper citation networks, or author collaboration networks. These networks involve not only direct relationships between papers or authors, but also a broader system of dependencies between the references of papers connected through multiple simultaneous citation links. In this work, we extend recently developed relational hyperevent models (RHEM) to analyze scientific networks - systems of scientific publications connected by citations and authorship. We introduce new covariates that represent theoretically relevant and empirically meaningful sub-network configurations. The new model specification supports testing of hypotheses that align with the polyadic nature of scientific publication events and the multiple interdependencies between authors and references of current and prior papers. We implement the model using open-source software to analyze a large, publicly available scientific network dataset. A significant finding of the study is the tendency for subsets of papers to be repeatedly cited together across publications. This result is crucial as it suggests that the papers' impact may be partly due to endogenous network processes. More broadly, the study shows that models accounting for both the hyperedge structure of publication events and the interconnections between authors and references significantly enhance our understanding of the network mechanisms that drive scientific production, productivity, and impact.

翻译：适用于文献计量网络分析的统计模型发展，落后于近期科学学研究展现的实证雄心。现有研究通常将分析焦点局限于论文引用网络或作者合作网络。这些网络不仅涉及论文或作者之间的直接关系，还包含通过多重同步引用链接相连的论文参考文献间更广泛的依赖系统。本研究扩展了近期发展的关系超事件模型（RHEM），用于分析科学网络——即由引用关系和作者身份联结的科学出版物系统。我们引入了代表理论相关且实证有意义的子网络配置的新协变量。该新模型设定支持检验与科学出版事件的多元性质、以及当前及先前论文作者与参考文献间多重相互依赖关系相一致的假设。我们采用开源软件实现该模型，分析了一个大规模公开的科学网络数据集。研究的一项重要发现是，论文子集在跨出版物中倾向于被共同引用。这一结果至关重要，因为它表明论文的影响力可能部分源于内生网络过程。更广泛而言，研究表明，同时考虑出版事件的超边结构以及作者与参考文献间互联关系的模型，显著增强了我们对驱动科学生产、生产率和影响力的网络机制的理解。