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cs.LG2024
FSW-GNN: A Bi-Lipschitz WL-Equivalent Graph Neural Network
Yonatan Sverdlov, Yair Davidson, Nadav Dym +1
Famously, the ability of Message Passing Neural Networks (MPNN) to distinguish between graphs is limited to graphs separable by the Weisfeiler-Lemann (WL) graph isomorphism test, a…
cs.LG2024
Revisiting Multi-Permutation Equivariance through the Lens of Irreducible Representations
Yonatan Sverdlov, Ido Springer, Nadav Dym
This paper explores the characterization of equivariant linear layers for representations of permutations and related groups. Unlike traditional approaches, which address these pro…
cs.LG2024
On the Expressive Power of Sparse Geometric MPNNs
Yonatan Sverdlov, Nadav Dym
Motivated by applications in chemistry and other sciences, we study the expressive power of message-passing neural networks for geometric graphs, whose node features correspond to…