# Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting

arXiv: https://arxiv.org/abs/2108.12238 · HTML: https://mlsanity.com/paper/2108.12238/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [DeeperGCN: All You Need to Train Deeper GCNs](https://mlsanity.com/paper/2006.07739/) (2020)
- [PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting](https://mlsanity.com/paper/2002.12898/) (2020)
- [Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction](https://mlsanity.com/paper/1911.10516/) (2019)
- [ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling](https://mlsanity.com/paper/1903.05631/) (2019)
- [Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality](https://mlsanity.com/paper/1711.00939/) (2017)
- [HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method](https://mlsanity.com/paper/2101.04264/) (2021)
- [Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results](https://mlsanity.com/paper/2010.07474/) (2020)

## Built on by (most cited first, 30 of 32)

- [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)
- [AirFormer: Predicting Nationwide Air Quality in China with Transformers](https://mlsanity.com/paper/2211.15979/) (2022)
- [Graph Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2310.15978/) (2023)
- [AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction](https://mlsanity.com/paper/2402.03784/) (2024)
- [STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model](https://mlsanity.com/paper/2403.12418/) (2024)
- [Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting](https://mlsanity.com/paper/2305.19183/) (2023)
- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [WeatherGNN: Exploiting Meteo- and Spatial-Dependencies for Local Numerical Weather Prediction Bias-Correction](https://mlsanity.com/paper/2310.05517/) (2023)
- [A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification](https://mlsanity.com/paper/2410.22377/) (2024)
- [Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer](https://mlsanity.com/paper/2201.06095/) (2022)
- [Machine Learning for Urban Air Quality Analytics: A Survey](https://mlsanity.com/paper/2310.09620/) (2023)
- [Graph Neural Network for spatiotemporal data: methods and applications](https://mlsanity.com/paper/2306.00012/) (2023)
- [MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting](https://mlsanity.com/paper/2308.15840/) (2023)
- [Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting](https://mlsanity.com/paper/2111.04942/) (2021)
- [Spatiotemporal Causal Decoupling Model for Air Quality Forecasting](https://mlsanity.com/paper/2505.20119/) (2025)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Modeling Time-Series and Spatial Data for Recommendations and Other Applications](https://mlsanity.com/paper/2212.13259/) (2022)
- [TPRNN: A Top-Down Pyramidal Recurrent Neural Network for Time Series Forecasting](https://mlsanity.com/paper/2312.06328/) (2023)
- [AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks](https://mlsanity.com/paper/2501.13141/) (2025)
- [ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2509.02217/) (2025)
- [MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting](https://mlsanity.com/paper/2509.03852/) (2025)
- [Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting](https://mlsanity.com/paper/2601.21899/) (2026)
- [AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecasting](https://mlsanity.com/paper/2603.17529/) (2026)
- [Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data](https://mlsanity.com/paper/2401.00521/) (2023)
- [ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution](https://mlsanity.com/paper/2403.01738/) (2024)
- [Spatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided Encoder-Decoder Architecture](https://mlsanity.com/paper/2412.13935/) (2024)
- [Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network](https://mlsanity.com/paper/2504.10014/) (2025)
- [MVAR: MultiVariate AutoRegressive Air Pollutants Forecasting Model](https://mlsanity.com/paper/2507.12023/) (2025)
- [Ada-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/2508.17867/) (2025)

## Abstract (arXiv)

The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air pollution in advance. Previous works focus on citywide air quality forecasting and cannot solve nationwide city forecasting problem, whose difficulties lie in capturing the latent dependencies between geographically distant but highly correlated cities. In this paper, we propose the group-aware graph neural network (GAGNN), a hierarchical model for nationwide city air quality forecasting. The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively. GAGNN introduces differentiable grouping network to discover the latent dependencies among cities and generate city groups. Based on the generated city groups, a group correlation encoding module is introduced to learn the correlations between them, which can effectively capture the dependencies between city groups. After the graph construction, GAGNN implements message passing mechanism to model the dependencies between cities and city groups. The evaluation experiments on Chinese city air quality dataset indicate that our GAGNN outperforms existing forecasting models.