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