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

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

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 st

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