mlsanityData to 14 Oct 2026

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

arXiv 2104.07788 ↗First posted 15 Apr 2021cs.LG cs.AI cs.SIcited by 118

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Abstract (arXiv)

We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of the library is to make temporal geometric deep learning available for researchers and machine learning practitioners in a unified easy-to-use framework. PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch eco-system, streamlined neural network layer definitions, temporal snapshot generators for batching, and integrated benchmark datasets. These features are illustrated with a tutorial-like case study. Experiments demonstrate the predictive performance of the models implemented in the library on real world problems such as epidemiological forecasting, ridehail demand prediction and web-traffic management. Our sensitivity analysis of runtime shows that the framework can potentially operate on web-scale datasets with rich temporal features and spatial structure.

Key facts. Cited by 118 papers in the map (data through 14 Oct 2026). Builds on 35 of its 72 references that are in the map. First posted on arXiv 15 Apr 2021.

Builds on

35 of 72 references.

References · 35

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23 more
  1. 2015Gated Graph Sequence Neural Networkscited by 796
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  3. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 20; 2023: 28; 2024: 34; 2025: 15; 2026: 12.

118 papers.

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  1. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 157
  2. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 110
  3. 2023DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusioncited by 87
  4. 2022TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphscited by 61
  5. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 61
  6. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
  7. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 30
  8. 2022MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplaciancited by 28
  9. 2024DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphscited by 27
  10. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  11. 2023Neurosymbolic AI for Reasoning over Knowledge Graphs: A Surveycited by 21
  12. 2023Deep learning for dynamic graphs: models and benchmarkscited by 19
38 more
  1. 2022PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphscited by 18
  2. 2023Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stationscited by 18
  3. 2022A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problemcited by 17
  4. 2021Graph Neural Networks: Methods, Applications, and Opportunitiescited by 16
  5. 2022Instant Graph Neural Networks for Dynamic Graphscited by 16
  6. 2022A Comprehensive Survey on Distributed Training of Graph Neural Networkscited by 15
  7. 2023Feature Transportation Improves Graph Neural Networkscited by 15
  8. 2022Black-box Bayesian inference for economic agent-based modelscited by 13
  9. 2023Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problemcited by 13
  10. 2021Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networkscited by 12
  11. 2021Non-separable Spatio-temporal Graph Kernels via SPDEscited by 11
  12. 2022ChemicalX: A Deep Learning Library for Drug Pair Scoringcited by 11
  13. 2023Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrologycited by 11
  14. 2024UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphscited by 10
  15. 2025Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modelingcited by 10
  16. 2022Forecasting Unobserved Node States with spatio-temporal Graph Neural Networkscited by 9
  17. 2024On The Temporal Domain of Differential Equation Inspired Graph Neural Networkscited by 9
  18. 2024SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networkscited by 9
  19. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  20. 2021Integrating LSTMs and GNNs for COVID-19 Forecastingcited by 8
  21. 2024Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passingcited by 8
  22. 2021A Unified View of Relational Deep Learning for Drug Pair Scoringcited by 7
  23. 2022Calibrating Agent-based Models to Microdata with Graph Neural Networkscited by 7
  24. 2022Time-aware Random Walk Diffusion to Improve Dynamic Graph Learningcited by 7
  25. 2024GraphStorm: all-in-one graph machine learning framework for industry applicationscited by 7
  26. 2024Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenariocited by 7
  27. 2024Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarkscited by 7
  28. 2023Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecastingcited by 6
  29. 2023NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streamscited by 6
  30. 2024Graph Neural Reaction Diffusion Modelscited by 6
  31. 2022Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarkscited by 4
  32. 2023PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUscited by 4
  33. 2023Structural Explanations for Graph Neural Networks using HSICcited by 4
  34. 2024An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimizationcited by 4
  35. 2026Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemicscited by 4
  36. 2021Crime Prediction with Graph Neural Networks and Multivariate Normal Distributionscited by 3
  37. 2024Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarkingcited by 3
  38. 2024Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectoriescited by 3

50 most cited of 118 All 118 (JSON)