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Morris, C.* ; Lipman, Y.* ; Maron, H.* ; Rieck, B. ; Kriege, N.M.* ; Grohe, M.* ; Fey, M.* ; Borgwardt, K.*

Weisfeiler and Leman go Machine Learning: The Story so far.

J. Mach. Learn. Res. 24:333 (2023)
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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
Language english
Publication Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 1532-4435
e-ISSN 1533-7928
Journal Journal of Machine Learning Research
Quellenangaben Volume: 24, Issue: , Pages: , Article Number: 333 Supplement: ,
Publisher MIT Press
Publishing Place 31 Gibbs St, Brookline, Ma 02446 Usa
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-540003-001
Grants Hightech Agenda Bavaria
Bavarian State Government
RWTH Junior Principal Investigator Fellowship under Germany's Excellence Strategy
WOS ID 001130675900001
Erfassungsdatum 2024-01-16
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