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Poster
in
Workshop: Bridging the Gap Between Practice and Theory in Deep Learning

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

Raffaele Paolino · Sohir Maskey · Pascal Welke · Gitta Kutyniok


Abstract: We introduce r-loopy Weisfeiler-Leman (r-WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, r-MPNNs, that can count cycles up to length r+2. Most notably, we show that r-WL 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-MPNNs on several synthetic datasets and present state-of-the-art predictive performance on various real-world datasets.

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