Efficient GNN Explanation via Learning Removal-based Attribution

Understand

As Graph Neural Networks (GNNs) have been widely used in real-world applications, model explanations are required not only by users but also by legal regulations.

• However, simultaneously achieving high fidelity and low computational costs in generating explanations has been a challenge for current methods.
• In this work, we propose a framework of GNN explanation named LeArn Removal-based Attribution (LARA) to address this problem.
• Specifically, we introduce removal-based attribution and demonstrate its substantiated link to interpretability fidelity theoretically and experimentally.