  The study of international relations by definition deals with
interdependencies among countries. One form of interdependence between
countries is the diffusion of country-level features, such as policies,
political regimes, or conflict. In these studies, the outcome variable tends to
be categorical, and the primary concern is the clustering of the outcome
variable among connected countries. Statistically, such clustering is studied
with spatial econometric models. This paper instead proposes the use of a
statistical network approach to model diffusion with a binary outcome variable.
Using statistical network instead of spatial econometric models allows for a
more natural specification of the diffusion process, assuming autocorrelation
in the outcomes rather than the corresponding latent variable, and it
simplifies the inclusion of temporal dynamics, higher level interdependencies
and interactions between network ties and country-level features. In our
simulations, the performance of the Stochastic Actor-Oriented Model (SAOM)
estimator is evaluated. Our simulation results show that spatial parameters and
coefficients on additional covariates in a static binary spatial autoregressive
model are accurately recovered when using SAOM, albeit on a different scale. To
demonstrate the use of this model, the paper applies the model to the
international diffusion of same-sex marriage.
