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Generalization limits of Graph Neural Networks in identity effects learning

Author(s): D' Inverno GA; Brugiapaglia S; Ravanelli M;

Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is closely linked to the Weisfeiler-Lehman (WL) test for graph isomorphism to which they have been proven equivale ...

Article GUID: 39426036


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