INGREX: An Interactive Explanation Framework for Graph Neural Networks
This addresses the need for better interpretability in GNNs for users in applications requiring model transparency, though it is incremental as it builds on existing explanation algorithms.
The authors tackled the problem of explaining Graph Neural Networks (GNN) predictions by introducing INGREX, an interactive framework that improves user comprehension over existing static methods, demonstrating its effectiveness in three common scenarios.
Graph Neural Networks (GNNs) are widely used in many modern applications, necessitating explanations for their decisions. However, the complexity of GNNs makes it difficult to explain predictions. Even though several methods have been proposed lately, they can only provide simple and static explanations, which are difficult for users to understand in many scenarios. Therefore, we introduce INGREX, an interactive explanation framework for GNNs designed to aid users in comprehending model predictions. Our framework is implemented based on multiple explanation algorithms and advanced libraries. We demonstrate our framework in three scenarios covering common demands for GNN explanations to present its effectiveness and helpfulness.