Explainable AI for Graph Neural Networks via Symbolic Representation Learning

Abstract

Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems. However, their inherent complexity often leads to a "black box" problem, where it is difficult to understand the reasoning behind their predictions. This paper introduces a novel approach to explain GNN predictions by learning symbolic representations of the graph structure. We propose a model that maps the GNN's activations to a symbolic representation, enabling the generation of human-readable explanations. This method addresses the interpretability challenge in GNNs, offering a pathway to trust and confidence in their predictions. The core claim is that by leveraging symbolic representation learning, we can transform opaque GNN behavior into understandable insights. The proposed mechanism provides a foundation for building more transparent and reliable GNN-based systems.