Weisfeiler and Leman go Machine Learning: The Story so far

Weisfeiler and Leman go Machine Learning: The Story so far

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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Authors
  • Morris, Christopher
  • Lipman, Yaron
  • Maron, Haggai
  • Rieck, Bastian
  • Kriege, Nils M.
  • Grohe, Martin
  • Fey, Matthias
  • Borgwardt, Karsten
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Shortfacts
Category
Technical Report (Working Paper)
Divisions
Data Mining and Machine Learning
Subjects
Kuenstliche Intelligenz
Theoretische Informatik
Publisher
CoRR arXiv
Date
2021
Official URL
https://arxiv.org/abs/2112.09992
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