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Weisfeiler and Leman go Machine Learning: The Story so far.
J. Mach. Learn. Res. 24:333 (2023)
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.
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Publication type
Article: Journal article
Document type
Review
Keywords
Machine learning for graphs; Graph neural networks; Weisfeiler-Leman algorithm; expressivity; equivariance; Sherali-adams Relaxations; Neural-network; Graph Isomorphism; Darc System; Kernels; Classification; Information; Generation; Logics
ISSN (print) / ISBN
1532-4435
e-ISSN
1533-7928
Quellenangaben
Volume: 24,
Article Number: 333
Publisher
MIT Press
Publishing Place
31 Gibbs St, Brookline, Ma 02446 Usa
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of AI for Health (AIH)
Grants
Hightech Agenda Bavaria
Bavarian State Government
RWTH Junior Principal Investigator Fellowship under Germany's Excellence Strategy
Bavarian State Government
RWTH Junior Principal Investigator Fellowship under Germany's Excellence Strategy