The Weisfeiler-Leman method is a classic heuristic for graph isomorphism testing, which iteratively encodes vertex neighborhoods of increasing radius by vertex colors. Two graphs whose vertex colors do not match are called non-isomorphic. The method is fundamental for recent advances in machine learning with graphs, e.g., graph kernels and graph neural networks. This contribution overviews the development of graph kernels based on the Weisfeiler-Leman algorithm, which are among the most successful graph kernels today. We describe the Weisfeiler-Leman heuristic for graph isomorphism testing, from which the classical Weisfeiler-Leman subtree kernel directly follows. Further, we summarize the theory of optimal assignment kernels and present the Weisfeiler-Leman optimal assignment kernel for graphs and the related Wasserstein Weisfeiler-Leman graph kernel. We discuss kernel functions based on the k-dimensional Weisfeiler-Leman algorithm, a strict generalization of the Weisfeiler-Leman heuristic. We show that a local, sparsity-aware variant of this algorithm can lead to scalable and expressive kernels. Moreover, we survey other kernels based on the principle of Weisfeiler-Leman refinement. Finally, we shed some light on the connection between Weisfeiler-Leman-based kernels and neural architectures for graph-structured input.

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