mlsanityData to 14 Oct 2026

Graph Neural Stochastic Differential Equations

arXiv 2308.12316 ↗First posted 23 Aug 2023cs.LGcited by 2

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Abstract (arXiv)

We present a novel model Graph Neural Stochastic Differential Equations (Graph Neural SDEs). This technique enhances the Graph Neural Ordinary Differential Equations (Graph Neural ODEs) by embedding randomness into data representation using Brownian motion. This inclusion allows for the assessment of prediction uncertainty, a crucial aspect frequently missed in current models. In our framework, we spotlight the textitLatent Graph Neural SDE variant, demonstrating its effectiveness. Through empirical studies, we find that Latent Graph Neural SDEs surpass conventional models like Graph Convolutional Networks and Graph Neural ODEs, especially in confidence prediction, making them superior in handling out-of-distribution detection across both static and spatio-temporal contexts.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 23 Aug 2023.

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19 of 40 references.

References · 19

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2018Neural Ordinary Differential Equationscited by 3,779
  3. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  5. 2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 283
  6. 2020Scalable Gradients for Stochastic Differential Equationscited by 271
  7. 2019Graph Neural Ordinary Differential Equationscited by 154
  8. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
  9. 2020Bayesian Graph Neural Networks with Adaptive Connection Samplingcited by 81
  10. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
  11. 2021Efficient and Accurate Gradients for Neural SDEscited by 71
  12. 2020Mat'ern Gaussian Processes on Graphscited by 66
7 more
  1. 2022GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generationcited by 43
  2. 2019NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Datacited by 42
  3. 2021Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equationscited by 38
  4. 2023Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecastingcited by 11
  5. 2013Gaussian Processes for Nonlinear Signal Processingcited by 9
  6. 2023Towards Complex Dynamic Physics System Simulation with Graph Neural ODEscited by 8
  7. 2023Graph Neural Stochastic Differential Equations for Learning Brownian Dynamicscited by 2

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Partially Stochastic Infinitely Deep Bayesian Neural Networkscited by 6
  2. 2025Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agendacited by 0