Integrating Graph Neural Networks and Fuzzy Logic to Enhance Deep Learning Interpretability

We propose a novel methodology that combines Graph Neural Networks (GNNs) with Fuzzy Logic to enhance the interpretability of deep learning models. GNNs handle structured data, while Fuzzy Logic provides a framework that excels in handling uncertainty and imprecision. To solve the challenge of interpretability in GNNs, we present a novel approach that marries GNNs’ expressive power with Fuzzy Logic’s readability. Preliminary experiments show promising results, indicating the potential of this approach to create AI systems that are transparent and trustworthy.