Methods › General › Air Quality Forecasting › GAGNN
Group-Aware Neural Network
GAGNN
Introduced by Ling Chen et al. in Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
GAGNN, or Group-aware Graph Neural Network, is 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.
Papers archive 2025-07-28
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Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting 27 Aug 2021 · 1 repository · arXiv:2108.12238
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Graph Neural Network | 1 |
| graph construction | 1 |
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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