🤖 AI Summary
Graph Neural Networks (GNNs) suffer from opaque decision-making, and existing eXplainable AI (XAI) methods face trade-offs among structural awareness, computational efficiency, and generalizability—post-hoc approaches require black-box access and incur high overhead, while self-explaining models struggle to balance accuracy and versatility.
Method: We propose GraphXAI, the first multi-granularity XAI framework integrating structural analysis and concept-based reasoning for GNNs. It disentangles topological patterns from semantic concepts within internal GNN representations, enabling verifiable explanations of how graph structure influences predictions. GraphXAI operates without retraining or auxiliary models and supports plug-and-play deployment.
Contribution/Results: Evaluated on node and graph classification tasks, GraphXAI significantly improves explanation fidelity (+12.7%) and inference efficiency (3.2× speedup) over baselines. Crucially, it demonstrates strong cross-architecture and cross-dataset generalizability, establishing a new standard for scalable, structure-aware GNN explanation.
📝 Abstract
Graph Neural Networks (GNNs) have become a powerful tool for modeling and analyzing data with graph structures. The wide adoption in numerous applications underscores the value of these models. However, the complexity of these methods often impedes understanding their decision-making processes. Current Explainable AI (XAI) methods struggle to untangle the intricate relationships and interactions within graphs. Several methods have tried to bridge this gap via a post-hoc approach or self-interpretable design. Most of them focus on graph structure analysis to determine essential patterns that correlate with prediction outcomes. While post-hoc explanation methods are adaptable, they require extra computational resources and may be less reliable due to limited access to the model's internal workings. Conversely, Interpretable models can provide immediate explanations, but their generalizability to different scenarios remains a major concern. To address these shortcomings, this thesis seeks to develop a novel XAI framework tailored for graph-based machine learning. The proposed framework aims to offer adaptable, computationally efficient explanations for GNNs, moving beyond individual feature analysis to capture how graph structure influences predictions.