mlsanityData to 14 Oct 2026

A Modal Logic for Explaining some Graph Neural Networks

arXiv 2307.05150 ↗First posted 11 Jul 2023cs.AI cs.LOcited by 0

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Abstract (arXiv)

In this paper, we propose a modal logic in which counting modalities appear in linear inequalities. We show that each formula can be transformed into an equivalent graph neural network (GNN). We also show that each GNN can be transformed into a formula. We show that the satisfiability problem is decidable. We also discuss some variants that are in PSPACE.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 25 references that are in the map. First posted on arXiv 11 Jul 2023.

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7 of 25 references.

References · 7

  1. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  2. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  3. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
  4. 2021The Logic of Graph Neural Networkscited by 85
  5. 2022Global Explainability of GNNs via Logic Combination of Learned Conceptscited by 65
  6. 2022DT+GNN: A Fully Explainable Graph Neural Network using Decision Treescited by 6
  7. 2013Facebook and the Epistemic Logic of Friendshipcited by 3

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