Explainable AI for Graph Structure Discovery

Abstract

Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems, by effectively learning patterns from graph-structured data. However, a significant limitation of GNNs is their inherent opaqueness—the "black box" nature of their decision-making process. This paper addresses this challenge by proposing a novel approach integrating an explainability layer directly into a GNN architecture. This layer aims to provide transparent and interpretable explanations for the network's predictions, focusing on identifying the key structural features within the graph that contribute to the outcome. Specifically, we introduce a mechanism to highlight influential nodes and edges, offering insights into *why* a GNN made a particular prediction. This work enhances the trustworthiness and usability of GNNs by promoting a deeper understanding of their internal workings. The core contribution lies in bridging the gap between predictive performance and interpretability in graph learning.