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

TL;DR

This paper introduces scalable, local variants of the Weisfeiler-Leman algorithm for graph embeddings, improving efficiency and expressiveness while reducing overfitting, with state-of-the-art results on benchmarks.

Contribution

It proposes local WL-based algorithms and neural architectures that are more scalable and expressive, addressing overfitting issues in higher-order graph embeddings.

Findings

Local algorithms reduce computation time significantly.

Kernel version achieves state-of-the-art graph classification results.

Neural version performs well on large-scale molecular regression.

Abstract

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.