Research map / Graph learning / GNN architectures
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add CMD regularizer on IID vs biased representations (ablation) | APPNP · PubMed | Micro-F1: 63.4 -> 69.4 | Helped |
| add instance reweighting (ablation) | APPNP · PubMed | Micro-F1: 63.4 -> 66.4 | Helped |
| combine regularization and instance reweighting (ablation) | SR-GNN · Cora | Micro-F1: 72.1 -> 73.5 | Helped |
| combine regularization and instance reweighting (ablation) | SR-GNN · PubMed | Micro-F1: 69.4 -> 71.3 | Helped |
| add instance reweighting on top of regularization (ablation) | SR-GNN · Citeseer | Micro-F1: 66.1 -> 67.1 | Helped |
| SR-GNN framework applied to GCN | GCN · PubMed | Micro-F1: 60.6 -> 67.0 | Helped |
| SR-GNN reweighting applied to DGI | DGI · PubMed | Micro-F1: 58.0 -> 62.0 | Helped |
| CMD regularizer instead of adversarial DANN (ablation) | APPNP · PubMed | Micro-F1: 67.8 -> 70.4 | Helped |
| SR-GNN vs APPNP on ogb-arxiv 1% labels | APPNP · ogb-arxiv | Accuracy: 59.8 -> 61.6 | Helped |
| SR-GNN vs GCN on reddit 1% labels | GCN · reddit | Accuracy: 89.7 -> 91.5 | Helped |
| larger lower bound B_l on instance weights (ablation) | SR-GNN · Cora/Citeseer/PubMed | Micro-F1 | Hurt |
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