  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 conflicts, for example, scholars hypothesize that membership
in geopolitical coalitions shapes the decision to engage in militarized
conflict. Such theories explain the ways in which nodal and dyadic
characteristics affect the evolution of relational ties 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 node membership in latent groups as well as the direct
formation of edges between dyads. While prior substantive research often
assumes the decision to engage in militarized conflict is independent across
states and static over time, we demonstrate that conflict patterns are 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.
