Research map / Graph learning / Knowledge and temporal graphs
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| synchronous localized spatial-temporal graph convolution (STSGCN) vs separate spatial/temporal modules | traffic forecasting · PEMS04 | MAE: 22.70 -> 21.19 | Helped |
| STSGCN vs DCRNN | traffic forecasting · PEMS07 | RMSE: 38.58 -> 39.03 | Hurt |
| use individual STSGCM per time period instead of shared module (ablation) | STSGCN · PEMS03 | MAE | Helped |
| GLU activation instead of ReLU (ablation) | STSGCN · PEMS03 | MAE | Helped |
| add spatial-temporal embeddings (ablation) | STSGCN · PEMS03 | MAE | Helped |
| add learnable mask matrix (ablation) | STSGCN · PEMS03 | MAE | Helped |
| per-timestep fully connected output layers instead of shared conv output (ablation) | STSGCN · PEMS03 | MAE | Helped |
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