Research map / Graph learning / GNN architectures
Towards Deep Attention in Graph Neural Networks: Problems and Remedies
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adding node-adaptive and negative hop attention plus layer-aggregated features (AERO-GNN) vs. baselines | attention-based GNNs on heterophilic/homophilic benchmarks · 12 node classification benchmarks | average ranking: A.R. 1.4 vs next best 5.2 (GPRGNN) | Helped |
| Replacing GATv2 propagation with GATv2 linear propagation (ablation) | GATv2 · 12 benchmarks (Table 3 subset) | accuracy: mixed small differences, e.g. Chameleon 69.06 vs 68.73 | No clear effect |
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