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. The explainer in LARA learns to generate removal-based attribution which enables providing explanations with high fidelity. A strategy of subgraph sampling is designed in LARA to improve the scalability of the training process. In the deployment, LARA can efficiently generate the explanation through a feed-forward pass. We benchmark our approach with other state-of-the-art GNN explanation methods on six datasets. Results highlight the effectiveness of our framework regarding both efficiency and fidelity. In particular, LARA is 3.5 times faster and achieves higher fidelity than the state-of-the-art method on the large dataset ogbn-arxiv (more than 160K nodes and 1M edges), showing its great potential in real-world applications. Our source code is available at https://anonymous.4open.science/r/LARA-10D8/README.md.

翻译：随着图神经网络（GNNs）在现实应用中的广泛部署，模型解释不仅成为用户需求，更受到法律规范的强制要求。然而，当前解释方法在实现高保真度的同时保持较低计算成本仍面临挑战。本文提出名为LeArn Removal-based Attribution（LARA）的GNN解释框架以解决该问题。具体而言，我们引入基于移除的归因方法，并从理论和实验两方面证明其与可解释性保真度的本质联系。LARA中的解释器通过学习生成基于移除的归因，从而提供高保真度的解释。为提升训练过程的可扩展性，我们设计了子图采样策略。在部署阶段，LARA可通过单次前向传播高效生成解释。我们在六个数据集上将该方法与当前最优GNN解释方法进行对比，结果证明了我们框架在效率与保真度两方面的有效性。特别地，在大规模数据集ogbn-arxiv（包含超16万个节点与100万条边）上，LARA的推理速度是现有最优方法的3.5倍，同时实现了更高保真度，展现了其在现实应用中的巨大潜力。我们的源代码见 https://anonymous.4open.science/r/LARA-10D8/README.md。