Research map / Graph learning / GNN architectures
A Metadata-Driven Approach to Understand Graph Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase Gini-Degree from 0.075 to 0.906 | GCN · Synthetic graph datasets | accuracy: 0.948 -> 0.798 | Hurt |
| Increase Gini-Degree from 0.075 to 0.906 | GAT · Synthetic graph datasets | accuracy: 0.746 -> 0.659 | Hurt |
| Increase Gini-Degree from 0.075 to 0.906 | GraphSAGE · Synthetic graph datasets | accuracy: 0.878 -> 0.76 | Hurt |
| Increase Gini-Degree from 0.075 to 0.906 | MoNet · Synthetic graph datasets | accuracy: 0.92 -> 0.672 | Hurt |
| Increase Gini-Degree from 0.075 to 0.906 | MixHop · Synthetic graph datasets | accuracy: 0.84 -> 0.804 | Hurt |
| Increase Gini-Degree from 0.075 to 0.906 | LINKX · Synthetic graph datasets | accuracy: 0.893 -> 0.832 | Hurt |
| Vary Gini-Degree from 0.075 to 0.906 | MLP · Synthetic graph datasets | accuracy: 0.705 -> 0.595 | No clear effect |
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