Papers › Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Christopher Morris, Gaurav Rattan, Petra Mutzel
Graph kernels based on the $1$-dimensional Weisfeiler-Leman algorithm and corresponding neural architectures recently emerged as powerful tools for (supervised) learning with graphs. However, due to the purely local nature of the algorithms, they might miss essential patterns in the given data and can only handle binary relations. The k-dimensional Weisfeiler-Leman algorithm addresses this by considering k-tuples, defined over the set of vertices, and defines a suitable notion of adjacency between these vertex tuples. Hence, it accounts for the higher-order interactions between vertices. However, it does not scale and may suffer from overfitting when used in a machine learning setting. Hence, it remains an important open problem to design WL-based graph learning methods that are simultaneously expressive, scalable, and non-overfitting. Here, we propose local variants and corresponding neural architectures, which consider a subset of the original neighborhood, making them more scalable, and less prone to overfitting. The expressive power of (one of) our algorithms is strictly higher than the original algorithm, in terms of ability to distinguish non-isomorphic graphs. Our experimental study confirms that the local algorithms, both kernel and neural architectures, lead to vastly reduced computation times, and prevent overfitting. The kernel version establishes a new state-of-the-art for graph classification on a wide range of benchmark datasets, while the neural version shows promising performance on large-scale molecular regression tasks.
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59a2828bfcee218d · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | ENZYMES | δ-2-LWL | Accuracy | 58.2% | #33 of 54 | Archive leaderboard | report |
| Graph Classification | IMDb-B | δ-2-LWL | Accuracy | 73.4% | #32 of 51 | Archive leaderboard | report |
| Graph Classification | IMDb-M | δ-2-LWL | Accuracy | 50.5% | #21 of 36 | Archive leaderboard | report |
| Graph Classification | NCI1 | δ-2-LWL | Accuracy | 85.5% | #9 of 69 | Archive leaderboard | report |
| Graph Classification | NCI109 | δ-2-LWL | Accuracy | 84.7 | #4 of 38 | Archive leaderboard | report |
| Graph Classification | PROTEINS | δ-2-LWL | Accuracy | 74.60% | #78 of 103 | Archive leaderboard | report |
| Graph Classification | PTC | δ-2-LWL | Accuracy | 62.70% | #30 of 37 | Archive leaderboard | report |
| Graph Classification | REDDIT-B | δ-2-LWL | Accuracy | 89.0 | #8 of 12 | Archive leaderboard | report |
| Graph Regression | ZINC-full | δ-2-GNN | Test MAE | 0.042±0.003 | #11 of 19 | Archive leaderboard | report |
| Graph Regression | ZINC-full | δ-2-LGNN | Test MAE | 0.045±0.006 | #12 of 19 | 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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