The self-attention mechanism has been adopted in various popular message passing neural networks (MPNNs), enabling the model to adaptively control the amount of information that flows along the edges of the underlying graph. Such attention-based MPNNs (Att-GNNs) have also been used as a baseline for multiple studies on explainable AI (XAI) since attention has steadily been seen as natural model interpretations, while being a viewpoint that has already been popularized in other domains (e.g., natural language processing and computer vision). However, existing studies often use naive calculations to derive attribution scores from attention, undermining the potential of attention as interpretations for Att-GNNs. In our study, we aim to fill the gap between the widespread usage of Att-GNNs and their potential explainability via attention. To this end, we propose GATT, edge attribution calculation method for self-attention MPNNs based on the computation tree, a rooted tree that reflects the computation process of the underlying model. Despite its simplicity, we empirically demonstrate the effectiveness of GATT in three aspects of model explanation: faithfulness, explanation accuracy, and case studies by using both synthetic and real-world benchmark datasets. In all cases, the results demonstrate that GATT greatly improves edge attribution scores, especially compared to the previous naive approach. Our code is available at https://github.com/jordan7186/GAtt.

翻译：自注意力机制已被多种主流消息传递神经网络（MPNNs）所采用，使模型能够自适应地控制沿底层图边流动的信息量。此类基于注意力的MPNNs（Att-GNNs）也常被用作可解释人工智能（XAI）多项研究的基线，因为注意力机制持续被视为天然模型解释手段，这一观点已在其他领域（如自然语言处理与计算机视觉）广泛普及。然而，现有研究常采用朴素计算方法从注意力中推导归因分数，削弱了注意力作为Att-GNNs解释工具的潜力。本研究旨在填补Att-GNNs的广泛应用与其通过注意力实现可解释性潜力之间的鸿沟。为此，我们提出GATT——一种基于计算树的自注意力MPNNs边归因计算方法，计算树是反映底层模型计算过程的根树结构。尽管方法简洁，我们通过合成与真实世界基准数据集，从模型解释的三个维度实证验证了GATT的有效性：忠实性、解释准确性与案例研究。所有实验结果均表明，GATT显著提升了边归因分数，尤其相较于先前朴素方法具有明显优势。代码已发布于https://github.com/jordan7186/GAtt。