Lecture Notes on Verifying Graph Neural Networks

In these lecture notes, we first recall the connection between graph neural networks, Weisfeiler-Lehman tests and logics such as first-order logic and graded modal logic. We then present a modal logic in which counting modalities appear in linear inequalities in order to solve verification tasks on graph neural networks. We describe an algorithm for the satisfiability problem of that logic. It is inspired from the tableau method of vanilla modal logic, extended with reasoning in quantifier-free fragment Boolean algebra with Presburger arithmetic.

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Journal
ArXiv.org
Published
2025-10-13
Primary Topic
Logic, Reasoning, and Knowledge
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article
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article

Lecture Notes on Verifying Graph Neural Networks

ArXiv.org
Logic, Reasoning, and Knowledge
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Lecture Notes on Verifying Graph Neural Networks

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Abstract

In these lecture notes, we first recall the connection between graph neural networks, Weisfeiler-Lehman tests and logics such as first-order logic and graded modal logic. We then present a modal logic in which counting modalities appear in linear inequalities in order to solve verification tasks on graph neural networks. We describe an algorithm for the satisfiability problem of that logic. It is inspired from the tableau method of vanilla modal logic, extended with reasoning in quantifier-free fragment Boolean algebra with Presburger arithmetic.

ArXiv.org
Reduced inequalities
Openalex Percentile: Top 24%
Logic, Reasoning, and Knowledge
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Lecture Notes on Verifying Graph Neural Networks · ArXiv.org (2025) | TGRS Research Map | TGRS