  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.
