Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning
MCML Authors
Gitta Kutyniok
Prof. Dr.
Principal Investigator
Abstract
Gitta Kutyniok
Prof. Dr.
Principal Investigator
Abstract
We introduce r-loopy Weisfeiler-Leman (r-ℓWL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, r-ℓMPNN, that can count cycles up to length r+2. Most notably, we show that r-ℓWL can count homomorphisms of cactus graphs. This strictly extends classical 1-WL, which can only count homomorphisms of trees and, in fact, is incomparable to k-WL for any fixed k. We empirically validate the expressive and counting power of the proposed r-ℓMPNN on several synthetic datasets and present state-of-the-art predictive performance on various real-world datasets.
inproceedings
NeurIPS 2024
38th Conference on Neural Information Processing Systems. Vancouver, Canada, Dec 10-15, 2024.
Authors
R. Paolino • S. Maskey • P. Welke • G. KutyniokLinks
URL GitHubResearch Area
BibTeXKey: PMW+24