GLIDE: Graph-guided Leap Inference for Diffusion Estimation of Spatio-Temporal Point Processes

• URL: http://arxiv.org/abs/2606.01273v1
• Date: Sun, 31 May 2026 14:56:24 GMT
• Title: GLIDE: Graph-guided Leap Inference for Diffusion Estimation of Spatio-Temporal Point Processes
• Abstract summary: We propose a conditional diffusion framework for temporal-temporal point processes (STPPs)<n>GLIDE organizes historical events into a multi-scale historical graph and encodes spatial topology through a dual-stream architecture.<n> Experiments on multiple real-world datasets show that GLIDE improves both distribution fitting and next-event prediction, with the largest gains appearing on the spatial side.
• Score: 19.756452407351986
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatio-temporal point processes (STPPs) provide a principled framework for modeling asynchronous events in continuous time and space. Recent diffusion-based approaches offer a flexible alternative to deterministic prediction by modeling complex conditional distributions, but their application to STPPs remains challenging: reverse sampling from pure noise is costly, and weak structural constraints in sparse spatial domains can lead to poorly localized probability mass. We propose \textbf{GLIDE} (Graph-guided Leap Inference for Diffusion Estimation), a conditional diffusion framework for next-event modeling in STPPs. GLIDE organizes historical events into a multi-scale historical graph and encodes temporal evolution and spatial topology through a dual-stream architecture, yielding a structured conditioning context for a dual-branch diffusion denoiser. It further introduces a prior-guided leap inference mechanism, in which a lightweight mean predictor provides a deterministic anchor and the reverse process starts from an intermediate diffusion step instead of from pure Gaussian noise. Experiments on multiple real-world datasets show that GLIDE improves both distribution fitting and next-event prediction, with the largest gains appearing on the spatial side. The results also indicate that prior-guided leap inference substantially reduces reverse-sampling cost while preserving the stochastic generation capability of diffusion models.

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