Research map / Graph learning / Graph algorithms and clustering
Weisfeiler-Lehman Meets Gromov-Wasserstein
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the WL distance instead of WWL distance | 1-NN graph classification · MUTAG, PROTEINS, PTC-FM, PTC-MR, IMDB-B, IMDB-M, COX2 | accuracy: MUTAG: 85.1 $\pm$ 6.5 -> 92.1 $\pm$ 6.3; PROTEINS: 64.7 $\pm$ 2.8 -> 63.0 $\pm$ 3.5; PTC-FM: 58.2 $\pm$ 8.5 -> 62.2 $\pm | Mixed |
| Use the WL distance instead of WWL kernel | SVM graph classification · MUTAG, PROTEINS, PTC-FM, PTC-MR, IMDB-B, IMDB-M, COX2 | accuracy: MUTAG: 85.3 $\pm$ 7.3 -> 89.9 $\pm$ 6.4; PROTEINS: 72.9 $\pm$ 3.6 -> 72.6 $\pm$ 3.1; PTC-FM: 62.2 $\pm$ 6.1 -> 62.1 $\pm | Mixed |
| Use the WLLB lower-bound distance instead of WWL kernel | SVM graph classification · MUTAG, PROTEINS, PTC-FM, PTC-MR, IMDB-B, IMDB-M, COX2 | accuracy: MUTAG: 85.3 $\pm$ 7.3 -> 90.0 $\pm$ 5.6; PROTEINS: 72.9 $\pm$ 3.6 -> 68.9 $\pm$ 1.9; PTC-FM: 62.2 $\pm$ 6.1 -> 59.6 $\pm | Mixed |
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