  In recent years, algorithms and neural architectures based on the
Weisfeiler--Leman algorithm, a well-known heuristic for the graph isomorphism
problem, have emerged as a powerful tool for machine learning with graphs and
relational data. Here, we give a comprehensive overview of the algorithm's use
in a machine-learning setting, focusing on the supervised regime. We discuss
the theoretical background, show how to use it for supervised graph and node
representation learning, discuss recent extensions, and outline the algorithm's
connection to (permutation-)equivariant neural architectures. Moreover, we give
an overview of current applications and future directions to stimulate further
research.
