Papers › Wasserstein Weisfeiler-Lehman Graph Kernels
Wasserstein Weisfeiler-Lehman Graph Kernels
Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten Borgwardt
Most graph kernels are an instance of the class of ℛ-Convolution kernels, which measure the similarity of objects by comparing their substructures. Despite their empirical success, most graph kernels use a naive aggregation of the final set of substructures, usually a sum or average, thereby potentially discarding valuable information about the distribution of individual components. Furthermore, only a limited instance of these approaches can be extended to continuously attributed graphs. We propose a novel method that relies on the Wasserstein distance between the node feature vector distributions of two graphs, which allows to find subtler differences in data sets by considering graphs as high-dimensional objects, rather than simple means. We further propose a Weisfeiler-Lehman inspired embedding scheme for graphs with continuous node attributes and weighted edges, enhance it with the computed Wasserstein distance, and thus improve the state-of-the-art prediction performance on several graph classification tasks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | D&D | WWL | Accuracy | 79.69% | #16 of 53 | Archive leaderboard | report |
| Graph Classification | ENZYMES | WWL | Accuracy | 59.13% | #31 of 54 | Archive leaderboard | report |
| Graph Classification | MUTAG | WWL | Accuracy | 87.27% | #48 of 74 | Archive leaderboard | report |
| Graph Classification | NCI1 | WWL | Accuracy | 85.75% | #8 of 69 | Archive leaderboard | report |
| Graph Classification | PROTEINS | WWL | Accuracy | 74.28% | #80 of 103 | Archive leaderboard | report |
| Graph Classification | PTC | WWL | Accuracy | 66.31% | #18 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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