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

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Graph Classification ENZYMES δ-2-LWL Accuracy 58.2% # 25
Graph Classification IMDb-B δ-2-LWL Accuracy 73.4% # 22
Graph Classification IMDb-M δ-2-LWL Accuracy 50.5% # 21
Graph Classification NCI1 δ-2-LWL Accuracy 85.5% # 8
Graph Classification NCI109 δ-2-LWL Accuracy 84.7 # 3
Graph Classification PROTEINS δ-2-LWL Accuracy 74.60% # 69
Graph Classification PTC δ-2-LWL Accuracy 62.70% # 30
Graph Classification REDDIT-B δ-2-LWL Accuracy 89.0 # 7


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