Original paper: Wasserstein Weisfeiler-Lehman Graph Kernels (NeurIPS 2019)
The codes are adapted from the accompanying code for the above paper. Please follow the README of that repository to install the dependencies.
Changes are made as follows:
-
Instead of manually downloading and decompressing the datasets, we use the DGL python library to fetch all the datasets that were used in the original paper.
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main.pyare modified to repeat cross-validation 10 times and report the average accuracy, similar to what the original paper did. -
Add an argument
--type, for users to choose whether run WWL discrete version (discrete), continuous version (continuous) or WWLsquare (both).
| MUTAG | PTC_MR | NCI1 | PROTEINS | D&D | ENZYMES | |
|---|---|---|---|---|---|---|
| WWL (from the paper) | 87.27±1.50 | 66.31±1.21 | 85.75±0.25 | 74.28±0.56 | 79.69±0.50 | 59.13±0.80 |
| WWL (our implementation) | 87.81±1.46 | 65.69±1.32 | 85.66±0.15 | 74.89±0.68 | 79.38±0.39 | 58.28±1.07 |
| ENZYMES | PROTEINS(_full) | BZR | COX2 | BZR_MD | COX2_MD | |
|---|---|---|---|---|---|---|
| WWL (from the paper) | 73.25±0.87 | 77.91±0.80 | 84.42±2.03 | 78.29±0.47 | 69.76±0.94 | 76.33±1.02 |
| WWL (our implementation) | 73.58±0.63 | 77.80±0.65 | 79.05±0.23 | 78.24±0.29 | 71.23±0.04 | 68.22±3.75 |
| WWL^2 | 74.30±0.59 | 77.60±0.52 | 78.72±0.87 | 81.15±1.65 | 71.32±0.35 | 66.82±2.70 |