# Spatio-Temporal Graph Neural Networks: A Survey

arXiv: https://arxiv.org/abs/2301.10569 · HTML: https://mlsanity.com/paper/2301.10569/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 25 of 25)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://mlsanity.com/paper/2103.07719/) (2021)
- [TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking](https://mlsanity.com/paper/2104.00194/) (2021)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs](https://mlsanity.com/paper/2203.14883/) (2022)
- [Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network](https://mlsanity.com/paper/2110.04038/) (2021)
- [TREND: TempoRal Event and Node Dynamics for Graph Representation Learning](https://mlsanity.com/paper/2203.14303/) (2022)
- [STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits](https://mlsanity.com/paper/1910.12906/) (2019)
- [FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting](https://mlsanity.com/paper/2007.15531/) (2020)
- [Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network](https://mlsanity.com/paper/2111.07258/) (2021)
- [Spatio-temporal graph neural networks for multi-site PV power forecasting](https://mlsanity.com/paper/2107.13875/) (2021)
- [Recurrent Space-time Graph Neural Networks](https://mlsanity.com/paper/1904.05582/) (2019)
- [Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention](https://mlsanity.com/paper/2204.11008/) (2022)
- [STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks](https://mlsanity.com/paper/2111.06750/) (2021)
- [Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data](https://mlsanity.com/paper/1909.04019/) (2019)
- [Space-Time Graph Neural Networks](https://mlsanity.com/paper/2110.02880/) (2021)
- [Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network](https://mlsanity.com/paper/2104.12518/) (2021)
- [Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting](https://mlsanity.com/paper/2202.10586/) (2022)

## Built on by (most cited first, 23 of 23)

- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions](https://mlsanity.com/paper/2401.09769/) (2024)
- [Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching](https://mlsanity.com/paper/2402.04924/) (2024)
- [A Survey of Link Prediction in Temporal Networks](https://mlsanity.com/paper/2502.21185/) (2025)
- [STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting](https://mlsanity.com/paper/2404.05774/) (2024)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Improving Graph Neural Networks by Learning Continuous Edge Directions](https://mlsanity.com/paper/2410.14109/) (2024)
- [Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation](https://mlsanity.com/paper/2411.03223/) (2024)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attention](https://mlsanity.com/paper/2512.13758/) (2025)
- [Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models](https://mlsanity.com/paper/2601.12083/) (2026)
- [Multi-Temporal Relationship Inference in Urban Areas](https://mlsanity.com/paper/2306.08921/) (2023)
- [Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification](https://mlsanity.com/paper/2410.09307/) (2024)
- [Explainable Spatio-Temporal GCNNs for Irregular Multivariate Time Series: Architecture and Application to ICU Patient Data](https://mlsanity.com/paper/2411.01070/) (2024)
- [FlowNet: Modeling Dynamic Spatio-Temporal Systems via Flow Propagation](https://mlsanity.com/paper/2511.05595/) (2025)
- [FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting](https://mlsanity.com/paper/2603.01657/) (2026)
- [Dynamic Sheaf Diffusion Networks with Adaptive Local Structure for Heterogeneous Spatio-Temporal Graph Learning](https://mlsanity.com/paper/2604.11275/) (2026)
- [Adaptive Graph Pruning with Sudden-Events Evaluation for Traffic Prediction using Online Semi-Decentralized ST-GNNs](https://mlsanity.com/paper/2512.17352/) (2025)
- [Efficient Prompt Learning for Traffic Forecasting](https://mlsanity.com/paper/2605.08273/) (2026)
- [Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems](https://mlsanity.com/paper/2606.09432/) (2026)
- [Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection](https://mlsanity.com/paper/2606.17109/) (2026)

## Abstract (arXiv)

Graph Neural Networks have gained huge interest in the past few years. These powerful algorithms expanded deep learning models to non-Euclidean space and were able to achieve state of art performance in various applications including recommender systems and social networks. However, this performance is based on static graph structures assumption which limits the Graph Neural Networks performance when the data varies with time. Spatiotemporal Graph Neural Networks are extension of Graph Neural Networks that takes the time factor into account. Recently, various Spatiotemporal Graph Neural Network algorithms were proposed and achieved superior performance compared to other deep learning algorithms in several time dependent applications. This survey discusses interesting topics related to Spatiotemporal Graph Neural Networks, including algorithms, applications, and open challenges.