Longitudinal Network Modeling of International Relations Data

Empirical analyses of international relations data have become one of the principal methods by which researchers evaluate theories of trade, conflict and other interactions between countries. For example, regression modeling has recently been used as a method of evaluating the question of whether or not the community of democratic countries is inherently peaceful. The data used in these analyses are inherently longitudinal, involving measured relations between nations over time. Despite this fact, and that many of the core approaches in scientifically oriented studies of international politics spring from strong policy concerns, very seldom do these statistical modeling efforts account for the temporal nature of the data, or attempt to gauge the validity of the obtained model-fitting results by comparison to unfolding events. To address these issues, we will develop and implement statistical models for relational data that take into account (a) the complex dependencies inherent to relational, social network data, and (b) the evolution of international relations over time. This will be done by extending regression and latent factor models for network data to the time domain, allowing for the analysis of complicated longitudinal relational data using tools that are familiar to social science researchers. We will leverage the longitudinal nature of the data to evaluate candidate statistical modeling approaches and estimation methods, and will use the methodology to better understand the dynamics of international conflict and trade data.