Research map / Graph learning / GNN architectures
Graph Random Neural Features for Distance-Preserving Graph Representations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GRNF as the untrained graph-network input layer instead of the topology-free baseline | GRNF classification model · NCI1 | accuracy: 69.8$\pm$2.2 -> 66.7$\pm$2.4 | Hurt |
| Use GRNF as the untrained graph-network input layer instead of the topology-free baseline | GRNF classification model · PROTEINS | accuracy: 75.8$\pm$3.7 -> 75.1$\pm$2.6 | No clear effect |
| Use GRNF as the untrained graph-network input layer instead of the topology-free baseline | GRNF classification model · ENZYMES | accuracy: 65.2$\pm$6.4 -> 45.9$\pm$6.6 | Hurt |
| Use GRNF as the untrained graph-network input layer instead of the topology-free baseline | GRNF classification model · IMDB-BINARY | accuracy: 50.7$\pm$2.4 -> 69.7$\pm$3.8 | Helped |
| Use GRNF as the untrained graph-network input layer instead of the topology-free baseline | GRNF classification model · IMDB-MULTI | accuracy: 36.1$\pm$3.0 -> 44.4$\pm$3.9 | Helped |
| Use GRNF as the untrained graph-network input layer instead of the topology-free baseline | GRNF classification model · COLLAB | accuracy: 55.0$\pm$1.9 -> 73.2$\pm$2.5 | Helped |
| Increase embedding dimension M | GRNF · SBM and Del | classification accuracy | Helped |
| Increase embedding dimension M | GRNF · ENZYMES and IMDB-BINARY | classification accuracy | No clear effect |
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