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