Research map / Graph learning / GNN architectures
Graph Structure Learning with Variational Information Bottleneck
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add VIB-GSL graph structure learning to GIN | GIN · IMDB-B | accuracy: 72.1 -> 77.1 | Helped |
| Add VIB-GSL graph structure learning to GIN | GIN · IMDB-M | accuracy: 49.7 -> 55.6 | Helped |
| Add VIB-GSL graph structure learning to GIN | GIN · REDDIT-B | accuracy: 85.4 -> 88.5 | Helped |
| Add VIB-GSL graph structure learning to GIN | GIN · COLLAB | accuracy: 78.8 -> 79.3 | Helped |
| Learn a new graph structure independently of the observed adjacency | VIB-GSL · REDDIT-B with edge perturbations | classification accuracy robustness | Helped |
| Increase the information-compression coefficient β (ablation) | VIB-GSL · IMDB-B and REDDIT-B | accuracy | Mixed |
| Use dataset-specific β values for the prediction-compression tradeoff (ablation) | VIB-GSL · IMDB-B and REDDIT-B | accuracy | Helped |
| Use the tractable variational approximation instead of SIB's MI-estimation bi-level optimization | VIB-GSL versus SIB · REDDIT-B training | training stability | Helped |
Closest papers by meaning
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- On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks (ICML 2025 · similarity 0.87)
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