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cs.AI2025
From Neural Networks to Logical Theories: The Correspondence between Fibring Modal Logics and Fibring Neural Networks
Ouns El Harzli, Bernardo Cuenca Grau, Artur d'Avila Garcez +2
Fibring of modal logics is a well-established formalism for combining countable families of modal logics into a single fibred language with common semantics, characterized by fibre…
cs.LG2025
Logical Expressivity and Explanations for Monotonic GNNs with Scoring Functions
Matthew Morris, David J. Tena Cucala, Bernardo Cuenca Grau
Graph neural networks (GNNs) are often used for the task of link prediction: predicting missing binary facts in knowledge graphs (KGs). To address the lack of explainability of GNN…
cs.AI2025
The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic
Bernardo Cuenca Grau, Eva Feng, Przemysław Andrzej Wałęga
Graph Neural Networks (GNNs) address two key challenges in applying deep learning to graph-structured data: they handle varying size input graphs and ensure invariance under graph…