mlsanityData to 7 Oct 2026

Temporal Graph ODEs for Irregularly-Sampled Time Series

arXiv 2404.19508 ↗First posted 30 Apr 2024cs.LGcited by 11

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Abstract (arXiv)

Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e.g., social networks and physical systems are characterized by continuous dynamics and sporadic observations. To address this limitation, we introduce the Temporal Graph Ordinary Differential Equation (TG-ODE) framework, which learns both the temporal and spatial dynamics from graph streams where the intervals between observations are not regularly spaced. We empirically validate the proposed approach on several graph benchmarks, showing that TG-ODE can achieve state-of-the-art performance in irregular graph stream tasks.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 44 of its 57 references that are in the map. First posted on arXiv 30 Apr 2024.

Builds on

44 of 57 references.

References · 44

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2018Neural Ordinary Differential Equationscited by 3,733
  6. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  7. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  8. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  9. 2019Simplifying Graph Convolutional Networkscited by 1,470
  10. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  11. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  12. 2020Neural Controlled Differential Equations for Irregular Time Seriescited by 501
32 more
  1. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  2. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  3. 2017Stable Architectures for Deep Neural Networkscited by 340
  4. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 273
  5. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  6. 2021GRAND: Graph Neural Diffusioncited by 258
  7. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
  8. 2019Graph Neural Ordinary Differential Equationscited by 153
  9. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
  10. 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
  11. 2022Graph-Coupled Oscillator Networkscited by 123
  12. 2019Fake News Detection on Social Media using Geometric Deep Learningcited by 115
  13. 2017Topology Adaptive Graph Convolutional Networkscited by 112
  14. 2019A Gentle Introduction to Deep Learning for Graphscited by 107
  15. 2021PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equationscited by 106
  16. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
  17. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  18. 2020Learning Continuous System Dynamics from Irregularly-Sampled Partial Observationscited by 60
  19. 2021Dissecting the Diffusion Process in Linear Graph Convolutional Networkscited by 58
  20. 2022Anti-Symmetric DGN: a stable architecture for Deep Graph Networkscited by 58
  21. 2019Predicting Path Failure In Time-Evolving Graphscited by 54
  22. 2020A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecastingcited by 54
  23. 2022GREAD: Graph Neural Reaction-Diffusion Networkscited by 54
  24. 2022Scalable Spatiotemporal Graph Neural Networkscited by 52
  25. 2018GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Predictioncited by 47
  26. 2019Neural Dynamics on Complex Networkscited by 38
  27. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  28. 2023Deep learning for dynamic graphs: models and benchmarkscited by 19
  29. 2024Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FRONDcited by 16
  30. 2023Feature Transportation Improves Graph Neural Networkscited by 15
  31. 2024On The Temporal Domain of Differential Equation Inspired Graph Neural Networkscited by 9
  32. 2023Do We Need an Encoder-Decoder to Model Dynamical Systems on Networks?cited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 8; 2026: 2.

11 papers.

  1. 2025Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baselinecited by 54
  2. 2025On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learningcited by 42
  3. 2024On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systemscited by 26
  4. 2025Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networkscited by 14
  5. 2025Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamicscited by 6
  6. 2025A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graphcited by 1
  7. 2025OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learningcited by 1
  8. 2026Riemannian Liquid Spatio-Temporal Graph Networkcited by 1
  9. 2025Non-Dissipative Graph Propagation for Non-Local Community Detectioncited by 0
  10. 2025C3DE: Causal-Aware Collaborative Neural Controlled Differential Equation for Long-Term Urban Crowd Flow Predictioncited by 0
  11. 2026MissHyper: Restoring Clinical Synchronicity in Missingness-Guided Hypergraph Forecastingcited by 0