Weisfeiler and Leman go Machine Learning: The Story so far

• URL: http://arxiv.org/abs/2112.09992v1
• Date: Sat, 18 Dec 2021 20:14:11 GMT
• Title: Weisfeiler and Leman go Machine Learning: The Story so far
• Authors: Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten Borgwardt
• Abstract summary: We give a comprehensive overview of the Weisfeiler-Leman 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.
• Score: 41.069698435199705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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.

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