The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs
It addresses the problem of graph-based machine learning for researchers and practitioners, but is incremental as it reviews existing work.
The paper provides a comprehensive overview of using the Weisfeiler-Leman algorithm for machine learning with graphs, covering theoretical background, applications in supervised classification, and connections to neural architectures.
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.