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.
翻译:社会科学研究的首要目标是了解潜在集团成员资格如何预测网络演变的动态过程。例如,在模拟国际军事化冲突时,学者们假设地缘政治联盟成员资格决定参与冲突。这种理论解释交点和交点特点如何通过对集团成员资格的影响影响冲突模式的演变。为了帮助对这些论点进行经验测试,我们开发了一个动态网络数据模型,将隐藏的马尔科夫模式与混合成员模式的混合成员结构块状模型结合起来,确定网络结构背后的潜在集团。与现有模型不同,我们采用共同变量,预测潜在集团的动态节点成员资格,并直接形成冲突之间的边缘。虽然先前的实质性研究往往假设参与国际军事化冲突的决定是独立的,而且随着时间的停滞,但我们证明冲突是由国家在地缘政治集团中不断演变的成员资格驱动的。民主联盟国家之间的多变异性变化,对网络结构结构产生不同的影响。与现有模型不同,我们采用共同变量,预测潜在集团中的动态节点成员资格以及暗点之间的直接形成。虽然先前的实质性研究往往假定参与国际军事化冲突的决定是独立的,但我们证明冲突是由国家在地缘政治集团成员资格中不断演变的驱动。拟议方法,即以可变式的软件配置为软化的软件,通过软软件流分配,通过软软件流流到崩溃式的软件流流到崩溃式的版本,以进行。