mlsanityData to 14 Oct 2026

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

arXiv 2606.01273 ↗First posted 31 May 2026cs.LGcited by 0

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Abstract (arXiv)

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 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 37 references that are in the map. First posted on arXiv 31 May 2026.

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References · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  5. 2020Denoising Diffusion Implicit Modelscited by 9,781
  6. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  7. 2017Graph Attention Networkscited by 8,923
  8. 2022Scalable Diffusion Models with Transformerscited by 6,054
  9. 2022Classifier-Free Diffusion Guidancecited by 5,847
  10. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  11. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  12. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
14 more
  1. 2021How Attentive are Graph Attention Networks?cited by 835
  2. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  3. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 582
  4. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 337
  5. 2020Transformer Hawkes Processcited by 235
  6. 2019Neural Jump Stochastic Differential Equationscited by 150
  7. 2019Intensity-Free Learning of Temporal Point Processescited by 140
  8. 2023Leapfrog Diffusion Model for Stochastic Trajectory Predictioncited by 132
  9. 2019Fully Neural Network based Model for General Temporal Point Processescited by 122
  10. 2022Accelerating Diffusion Models via Early Stop of the Diffusion Processcited by 100
  11. 2020Neural Spatio-Temporal Point Processescited by 93
  12. 2023DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Modelscited by 81
  13. 2023Spatio-temporal Diffusion Point Processescited by 52
  14. 2021Neural Point Process for Learning Spatiotemporal Event Dynamicscited by 42

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