Papers › A Persistent Weisfeiler–Lehman Procedure for Graph Classification
A Persistent Weisfeiler–Lehman Procedure for Graph Classification
Bastian Rieck, Christian Bock, Karsten Borgwardt
The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks. However, its subtree features are not able to capture connected components and cycles, topological features known for characterising graphs. To extract such features, we leverage propagated node label information and transform unweighted graphs into metric ones. This permits us to augment the subtree features with topological information obtained using persistent homology, a concept from topological data analysis. Our method, which we formalise as a generalisation of Weisfeiler–Lehman subtree features, exhibits favourable classification accuracy and its improvements in predictive performance are mainly driven by including cycle information.
Code
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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 | MUTAG | P-WL-C | Mean Accuracy | 90.51 | #73 of 74 | Archive leaderboard | report |
| Graph Classification | PROTEINS | P-WL-UC | Accuracy | 75.36% | #67 of 103 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molhiv | P-WL | Ext. data | No | #19 of 43 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molhiv | P-WL | Number of params | 4600000 | #19 of 43 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molhiv | P-WL | Test ROC-AUC | 0.8039 ± 0.0040 | #19 of 43 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molhiv | P-WL | Validation ROC-AUC | 0.8279 ± 0.0059 | #19 of 43 | 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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