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Abstract The significance and influence of U.S. Supreme Court majority opinions derive in large part from opinions’ roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology—the citation exponential random graph model, for which we provide user-friendly software—enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of exogenous covariates, indicating that models of Supreme Court citations should incorporate both the effects of case characteristics and the structure of past citations. 

Since few states are able to produce all of their own military hardware, a majority of countries’ military systems rely on weapon imports. The structure of the international defense technology exchange network remains an important puzzle to understand, along with the factors that drive its evolution. Drawing on a political economy model of arms supply, we propose a new network-oriented explanation for the worldwide transactions of major conventional weapons in the period after World War II. Using temporal exponential random graph models, our dynamic approach illustrates how network dependencies and the relative weighting of economic versus security considerations vary over time. One of our major results is to demonstrate how security considerations started regaining importance over economic ones after 2001. Additionally, our model exhibits strong out-of-sample predictive performance, with network dependencies contributing to model improvement especially after the Cold War.