论文标题
深图模型的忠实解释
Faithful Explanations for Deep Graph Models
论文作者
论文摘要
本文研究了图形神经网络（GNNS）的忠实解释。首先，我们提供了一种新的通用方法，以正式表征GNNS解释的忠诚。它适用于现有的解释方法，包括特征属性和子图解释。其次，我们的分析和经验结果表明，特征归因方法无法捕获边缘特征的非线性效应，而现有的子图解释方法并非忠实。第三，我们用卷积核心}（kec）介绍\ emph {k-hop解释，这是一种新的解释方法，通过利用有关其邻接矩阵中的图形结构的信息及其\ emph {k-th} power，可以证明对原始GNN的忠诚。最后，我们对使用GNN的合成和现实世界数据集进行分类和异常检测任务的经验结果证明了我们方法的有效性。
This paper studies faithful explanations for Graph Neural Networks (GNNs). First, we provide a new and general method for formally characterizing the faithfulness of explanations for GNNs. It applies to existing explanation methods, including feature attributions and subgraph explanations. Second, our analytical and empirical results demonstrate that feature attribution methods cannot capture the nonlinear effect of edge features, while existing subgraph explanation methods are not faithful. Third, we introduce \emph{k-hop Explanation with a Convolutional Core} (KEC), a new explanation method that provably maximizes faithfulness to the original GNN by leveraging information about the graph structure in its adjacency matrix and its \emph{k-th} power. Lastly, our empirical results over both synthetic and real-world datasets for classification and anomaly detection tasks with GNNs demonstrate the effectiveness of our approach.
