Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relations
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.
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BibTeX
@misc{jian2026dynamic,
title = {{Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relations}},
author = {Jie Jian and Jiguo Cao and Owen G. Ward},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.08813},
eprint = {2609.08813},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.08813},
}
RIS
TY - UNPB
TI - Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relations
AU - Jie Jian
AU - Jiguo Cao
AU - Owen G. Ward
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.08813
UR - https://arxiv.org/abs/2609.08813
AB - 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.
ER -