mlsanityData to 7 Oct 2026

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

arXiv 1906.00121 ↗First posted 31 May 2019cs.LG stat.MLcited by 820

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Abstract (arXiv)

Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However, the explicit graph structure (relation) does not necessarily reflect the true dependency and genuine relation may be missing due to the incomplete connections in the data. Furthermore, existing methods are ineffective to capture the temporal trends as the RNNs or CNNs employed in these methods cannot capture long-range temporal sequences. To overcome these limitations, we propose in this paper a novel graph neural network architecture, Graph WaveNet, for spatial-temporal graph modeling. By developing a novel adaptive dependency matrix and learn it through node embedding, our model can precisely capture the hidden spatial dependency in the data. With a stacked dilated 1D convolution component whose receptive field grows exponentially as the number of layers increases, Graph WaveNet is able to handle very long sequences. These two components are integrated seamlessly in a unified framework and the whole framework is learned in an end-to-end manner. Experimental results on two public traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superior performance of our algorithm.

Key facts. Cited by 820 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 79; 2022: 108; 2023: 150; 2024: 184; 2025: 164; 2026: 135.

820 papers.

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  1. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 335
  2. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
  3. 2021Graph Self-Supervised Learning: A Surveycited by 289
  4. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  5. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 209
  6. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  7. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  8. 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 170
  9. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 156
  10. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  11. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 149
  12. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
38 more
  1. 2021Graph Learning: A Surveycited by 140
  2. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  3. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 135
  4. 2021Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 133
  5. 2021ETA Prediction with Graph Neural Networks in Google Mapscited by 130
  6. 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 126
  7. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 122
  8. 2022Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecastingcited by 117
  9. 2023LargeST: A Benchmark Dataset for Large-Scale Traffic Forecastingcited by 116
  10. 2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 113
  11. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 93
  12. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 89
  13. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  14. 2023DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Modelscited by 81
  15. 2024Spatial-Temporal Large Language Model for Traffic Predictioncited by 81
  16. 2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
  17. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  18. 2022AirFormer: Predicting Nationwide Air Quality in China with Transformerscited by 73
  19. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
  20. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  21. 2021Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecastingcited by 67
  22. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  23. 2022A Survey on Graph Representation Learning Methodscited by 66
  24. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  25. 2023PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputationcited by 62
  26. 2022Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Versioncited by 59
  27. 2022Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Versioncited by 59
  28. 2025Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baselinecited by 54
  29. 2022Scalable Spatiotemporal Graph Neural Networkscited by 52
  30. 2023ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputationcited by 50
  31. 2023MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecastingcited by 50
  32. 2021AutoCTS: Automated Correlated Time Series Forecasting -- Extended Versioncited by 48
  33. 2024ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Modelcited by 48
  34. 2023Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatmentcited by 47
  35. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
  36. 2022STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Predictioncited by 45
  37. 2022Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfercited by 43
  38. 2022GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networkscited by 43

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