Research map / Graph learning / GNN architectures
Random Walk Graph Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use RWNN (trainable hidden graphs with random walk kernel) instead of GIN | RWNN · MUTAG | accuracy (%): 84.7 -> 89.2 | Helped |
| use RWNN instead of GIN | RWNN · ENZYMES | accuracy (%): 59.6 -> 57.6 | Hurt |
| use RWNN instead of WL kernel | RWNN · NCI1 | accuracy (%): 84.8 -> 73.9 | Hurt |
| shorten walk length P from 3 to 1 (1-step vs 3-step RWNN) (ablation) | RWNN · 10 graph classification datasets | accuracy (%): MUTAG 88.6 -> 89.2; NCI1 73.9 -> 71.4 | No clear effect |
| increase walk length P (1 to 3) (ablation) | RWNN · REDDIT-MULTI-5K | accuracy (%): 51.7 -> 53.4 | Helped |
Closest papers by meaning
- Revisiting Random Walks for Learning on Graphs (ICLR 2025 · similarity 0.92)
- Random Search Neural Networks for Efficient and Expressive Graph Learning (NeurIPS 2025 · similarity 0.90)
- Non-convolutional graph neural networks. (NeurIPS 2024 · similarity 0.89)
- Towards Scalable and Deep Graph Neural Networks via Noise Masking (AAAI 2025 · similarity 0.88)
- Learning Long Range Dependencies on Graphs via Random Walks (ICLR 2025 · similarity 0.88)
- Almost Surely Asymptotically Constant Graph Neural Networks (NeurIPS 2024 · similarity 0.88)
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