``Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Militarized Conflicts.''



A primary goal of social science research is to understand how latent group memberships predict the dynamic process of social network evolution. In the modeling of international conflicts 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 social networks 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, is implemented through an open-source software package. (Last Revised, December 2019)


Olivella, Santiago, Adeline Lo, Tyler Pratt, and Kosuke Imai. ``NetMix: Mixed-membership Regression Stochastic Blockmodel for Networks.'' available through CRAN and Github.

© Kosuke Imai
 Last modified: Mon Dec 23 11:36:30 PST 2019