International political relations are hard to characterize, as they depend on the network of state relationships. Important political alliances between two countries are often made with the help of other countries, international conflicts often require mediation, efforts of countries to tackle complex issues often require coordination of many states. Hence, it is important to gauge the network structure of international relations when accessing relation between any pair of countries.

Many current approaches rely on existing alliances or conflicts to characterize government-to-government interactions at a given point in time, but these are often the outcomes of ongoing negotiations or mounting conflicts, and thus are more likely to characterize past relations, rather than relations of the current period. In recent years, the availability of data capturing day-to-day interactions of countries has increased dramatically, greatly increasing the number of dimensions to be captured, and providing scholars with an opportunity to explore the network of smaller-scale country-to-country interactions.

This thesis proposes a way to characterise state-to-state relations in a context of the whole network of international relations in a given year applying latent network approach proposed by Hoff, Raftery and Handcock (2002) to summarized Integrated Crisis Early Warning System (ICEWS) events dataset. Under the latent space framework the probability (magnitude) of a relation between countries depends on the positions of countries in an unobserved “”social space.”” These positions are estimated within a Bayesian framework, using Markov chain Monte Carlo procedures to infer latent positions. I validate the resulting measure of government-to-government relations by demonstrating that they are strong predictors of international trade, outperforming the most commonly used measured of state relations, known as the S-Score.