Papers › Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings

Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings

2 Apr 2019NeurIPS 2020 12arXiv:1904.01543archive 2025-07-28

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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Tasks

General ClassificationGraph ClassificationGraph LearningGraph Regression

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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