Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts

TL;DR

This paper introduces a dynamic stochastic blockmodel regression that combines hidden Markov models with mixed-membership models to analyze how evolving latent group memberships influence international militarized conflicts over time.

Contribution

It develops a novel dynamic network model incorporating covariates for latent group memberships and edge formation, with scalable inference methods and open-source implementation.

Findings

States' evolving memberships influence conflict patterns over time.

Monadic covariates like democracy levels affect coalition shifts.

The model demonstrates heterogeneity in conflict dynamics driven by latent group changes.

Abstract

A primary goal of social science research is to understand how latent group memberships predict the dynamic process of network evolution. In the modeling of international militarized conflicts, for instance, scholars hypothesize that membership in geopolitical coalitions shapes the decision to engage in conflict. Such theories explain the ways in which nodal and dyadic characteristics affect the evolution of conflict patterns over time via their effects on group memberships. To aid the empirical testing of these arguments, we develop a dynamic model of network data by combining a hidden Markov model with a mixed-membership stochastic blockmodel that identifies latent groups underlying the network structure. Unlike existing models, we incorporate covariates that predict dynamic node memberships in latent groups as well as the direct formation of edges between dyads. While prior substantive research often assumes the decision to engage in international militarized conflict is independent across states and static over time, we demonstrate that conflict is driven by states' evolving membership in geopolitical blocs. Changes in monadic covariates like democracy shift states between coalitions, generating heterogeneous effects on conflict over time and across states. The proposed methodology, which relies on a variational approximation to a collapsed posterior distribution as well as stochastic optimization for scalability, is implemented through an open-source software package.