Decidability of graph neural networks via logical characterizations
We present results concerning the expressiveness and decidability of a popular graph learning formalism, graph neural networks (GNNs), exploiting connections with logic. We use a family of recently-discovered decidable logics involving ``Presburger quantifiers''. We show how to use the...
| Auteurs principaux: | , , , |
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| Format: | Conference item |
| Langue: | English |
| Publié: |
Lipics
2024
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