Weisfeiler and Leman go Machine Learning: The Story so far

Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten Borgwardt

Veröffentlichungen: Working PaperPreprint

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.
OriginalspracheEnglisch
HerausgeberarXiv.org
PublikationsstatusVeröffentlicht - 18 Dez. 2021

ÖFOS 2012

  • 102019 Machine Learning
  • 102031 Theoretische Informatik

Zitationsweisen