A new approach to analyzing coevolving longitudinal networks in international relations

Abstract

Previous models of international conflict have suffered two shortfalls. They tend not to embody dynamic changes, focusing rather on static slices of behavior over time across a single relational dimension. These models have also been empirically evaluated in ways that assumed the independence of each country, when in reality they are searching for the interdependence among all countries. A number of approaches are available now for analyzing relational data such as international conflict in a network context and a number of these can even handle longitudinal relational data, but none are developed to the point of exploring how networks can coevolve over time. We illustrate a solution to the limitations of existing approaches and apply this novel, dynamic, network based approach to study the dependencies among the ebb and flow of daily international interactions using a newly developed, and openly available, database of events among nations.

Network Interdependencies and the Evolution of the International Arms Trade

“…To satisfy this need, we apply the ERGM and its extension for longitudinal data, the temporal ERGM (TERGM; Hanneke, Fu, and Xing 2010). For the modeling of longitudinal data, there are possible alternatives like the stochastic actor-based approach by Snijders (2017), the latent space approach by Hoff, Raftery, and Handcock (2002), and the bilinear autoregression model by Minhas, Hoff, and Ward (2016). TERGMs are a simple, efficient, and valid tool for modeling large networks that exhibit strong structural inertia, have complex nested triadic structures, and change over discrete time periods (i.e., yearly).…”

Detecting reciprocity at a global scale

“…Although we can study the relationship of a country pair in isolation, these dyadic relationships also exist as part of a web of interstate relations ( 27 , 30 , 31 , 34 , 35 ). In particular, previous work has considered international relations as a network and examined network properties, such as degree centrality ( 43 , 44 ), in relation to a country’s willingness to adopt international environmental policy.…”

“…Unfortunately, many existing methods, usually based on Granger causality, are ill-suited to detecting such coupling because they require the influence of each variable to be linearly separable ( 33 ). A second challenge is that many models for detecting reciprocity can reach the wrong conclusion if they ignore the fact that relations between pairs of states typically exist within a broader web of interstate relations ( 27 , 30 , 31 , 34 , 35 ). These methodological difficulties, when combined, may help explain why empirical research has yet to confirm many of the predictions made by evolutionary models of international cooperation.…”

Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model

“…For example, Ward and Hoff (2007) model ties at each time period as a function of latent covariates (see also Ward, Ahlquist, and Rozenas 2013;Durante and Dunson 2014). More recently, Hoff et al (2015) provides a method that combines the dependence representation of the agent-based approach with the node heterogeneity of latent space approaches (see also Minhas, Hoff, and Ward 2016).…”