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.
翻译:本讲义首先回顾了图神经网络、Weisfeiler-Lehman测试与一阶逻辑及分级模态逻辑等逻辑系统之间的关联。随后提出一种包含线性不等式计数模态的模态逻辑,用于解决图神经网络的验证任务。我们描述了该逻辑可满足性问题的判定算法,其设计灵感源于经典模态逻辑的表推演方法,并扩展了包含Presburger算术的无量词布尔代数片段推理机制。