Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relations

2026-09-08

AI Summary

研究提出了一种动态潜在空间模型，用于处理非均匀泊松过程的连续时间关系事件数据，通过B-样条模拟时变潜在距离，并应用到国际关系中揭示合作模式。

Abstract

We study continuous-time relational event data, where time-stamped dyadic interactions reflect both individual node propensities and evolving relational proximity. We propose a dynamic latent space model for inhomogeneous Poisson processes, where event intensities depend on node-specific activity parameters and time-varying latent distances modeled via flexible B-splines. We prove model identifiability by decoupling baseline activity from latent position, ensuring high interaction volumes do not warp the spatial map. For scalability, we develop a minibatch stochastic gradient algorithm with stable initialization and geometric anchoring, alongside an effective-degrees-of-freedom BIC for tuning model complexity. Simulations confirm accurate parameter recovery and out-of-sample prediction. Applied to cooperative diplomatic events among 60 major economies (1995--2022), the model uncovers shifting patterns of international cooperation and isolates mobile geopolitical actors from stationary institutional anchors.

Problem

• Dynamic Latent Space
• Inhomogeneous Poisson Processes
• Relational Event Data
• International Relations

Innovation

• dynamic latent space model
• inhomogeneous Poisson processes
• flexible B-splines
• minibatch stochastic gradient algorithm
• geometric anchoring