Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for explaining GNNs typically rely on training auxiliary models, resulting in the explanations remain black-boxed. This paper introduces Graph Output Attribution (GOAt), a novel method to attribute graph outputs to input graph features, creating GNN explanations that are faithful, discriminative, as well as stable across similar samples. By expanding the GNN as a sum of scalar products involving node features, edge features and activation patterns, we propose an efficient analytical method to compute contribution of each node or edge feature to each scalar product and aggregate the contributions from all scalar products in the expansion form to derive the importance of each node and edge. Through extensive experiments on synthetic and real-world data, we show that our method not only outperforms various state-ofthe-art GNN explainers in terms of the commonly used fidelity metric, but also exhibits stronger discriminability, and stability by a remarkable margin.

通过将GNN扩展为涉及节点特征、边特征和激活模式的标量积的总和，我们提出了一种高效的分析方法来计算每个节点或边特征对每个标量积的贡献，并通过聚合展开形式中所有标量积的贡献来推导出每个节点和边的重要性，从而创造出忠实、有区别且在相似样本之间稳定的GNN解释。通过在合成和真实世界数据上进行大量实验证明，我们的方法不仅在常用的准确度度量上优于各种最先进的GNN解释器，而且在区分度和稳定性方面的优势明显。

GOAt：通过图输出归属解释图神经网络