Research map / Graph learning / Graph algorithms and clustering
On Valid Optimal Assignment Kernels and Applications to Graph Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use WL optimal assignment instead of the WL convolution kernel | WL graph kernel · Mutag | accuracy: 86.0 -> 84.5 | Hurt |
| Use WL optimal assignment instead of the WL convolution kernel | WL graph kernel · PTC-MR | accuracy: 61.3 -> 63.6 | Helped |
| Use WL optimal assignment instead of the WL convolution kernel | WL graph kernel · NCI1 | accuracy: 85.8 -> 86.1 | Helped |
| Use WL optimal assignment instead of the WL convolution kernel | WL graph kernel · NCI109 | accuracy: 85.9 -> 86.3 | Helped |
| Use WL optimal assignment instead of the WL convolution kernel | WL graph kernel · Proteins | accuracy: 75.6 -> 76.4 | Helped |
| Use WL optimal assignment instead of the WL convolution kernel | WL graph kernel · D&D | accuracy: 79.0 -> 79.2 | No clear effect |
| Use WL optimal assignment instead of the WL convolution kernel | WL graph kernel · Enzymes | accuracy: 53.7 -> 59.9 | Helped |
| Use WL optimal assignment instead of the WL convolution kernel | WL graph kernel · Collab | accuracy: 79.1 -> 80.7 | Helped |
| Use WL optimal assignment instead of the WL convolution kernel | WL graph kernel · Reddit | accuracy: 80.8 -> 89.3 | Helped |
Closest papers by meaning
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- Generalizing Weisfeiler-Lehman Kernels to Subgraphs (ICLR 2025 · similarity 0.85)
- On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity (NeurIPS 2024 · similarity 0.85)
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