Papers › Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning

Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning

20 Mar 2024arXiv:2403.13749archive 2025-07-28

Raffaele Paolino, Sohir Maskey, Pascal Welke, Gitta Kutyniok

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. The code is available at https://github.com/RPaolino/loopy

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