Research map / Graph learning / GNN architectures
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add graph-size renormalization to distance-encoding PE (ablation) | GNN with distance-encoding PE · IMDB-BINARY | test accuracy: 63.95 -> 71.10 | Helped |
| add sqrt(n) renormalization to eigenvector PE (ablation) | GNN with eigenvector PE · IMDB-BINARY | test accuracy: 66.10 -> 67.80 | Helped |
| add sqrt(n) renormalization to eigenvector PE (ablation) | GNN with eigenvector PE · COLLAB | test accuracy: 74.77 -> 73.74 | Hurt |
| add n renormalization to distance-encoding PE (ablation) | GNN with distance-encoding PE · COLLAB | test accuracy: 75.02 -> 75.65 | No clear effect |
| add renormalization of PEs by graph size (ablation) | GNN with eigenvector PE on synthetic graphs of varied sizes · synthetic regression with larger test graphs | test error | Helped |
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