GNN Explanation

Explaining the decisions of Graph Neural Networks (GNNs) is crucial for building trust and understanding in their applications. Current research focuses on developing methods that identify key subgraphs or structural motifs contributing to GNN predictions, often employing techniques like game theory (Shapley values), attention mechanisms, and generative models to create interpretable explanations. These efforts aim to address the "black box" nature of GNNs, improving model transparency and facilitating their use in high-stakes domains like healthcare and finance where understanding model decisions is paramount. A significant challenge remains in ensuring the robustness and reliability of these explanations, particularly in the face of adversarial attacks or noisy data.