Recently, Graph Neural Networks (GNNs) have significantly advanced the performance of machine learning tasks on graphs. However, this technological breakthrough makes people wonder: how does a GNN make such decisions, and can we trust its prediction with high confidence? When it comes to some critical fields, such as biomedicine, where making wrong decisions can have severe consequences, it is crucial to interpret the inner working mechanisms of GNNs before applying them. In this paper, we propose a model-agnostic model-level explanation method for different GNNs that follow the message passing scheme, GNNInterpreter, to explain the high-level decision-making process of the GNN model. More specifically, GNNInterpreter learns a probabilistic generative graph distribution that produces the most discriminative graph pattern the GNN tries to detect when making a certain prediction by optimizing a novel objective function specifically designed for the model-level explanation for GNNs. Compared to existing works, GNNInterpreter is more flexible and computationally efficient in generating explanation graphs with different types of node and edge features, without introducing another blackbox or requiring manually specified domain-specific rules. In addition, the experimental studies conducted on four different datasets demonstrate that the explanation graphs generated by GNNInterpreter match the desired graph pattern if the model is ideal; otherwise, potential model pitfalls can be revealed by the explanation.

翻译：近期，图神经网络（GNN）显著提升了图结构机器学习任务的性能。然而，这一技术突破引发了人们的思考：GNN如何做出决策？能否对其预测结果抱有高度信任？在生物医学等关键领域，错误决策可能造成严重后果，因此在应用GNN前解释其内部工作机制至关重要。本文针对遵循消息传递机制的不同GNN，提出了一种模型无关的模型级解释方法——GNNInterpreter，用于解释GNN模型的高层决策过程。具体而言，GNNInterpreter通过优化专为GNN模型级解释设计的新型目标函数，学习一个概率生成式图分布，该分布可生成GNN在做出特定预测时试图检测的最具判别性的图模式。与现有工作相比，GNNInterpreter在生成包含不同类型节点和边特征的解释图时更为灵活且计算高效，无需引入额外黑箱模型或手动指定领域特定规则。此外，在四个不同数据集上的实验研究表明：当模型理想时，GNNInterpreter生成的解释图与期望的图模式一致；反之，解释图可揭示模型潜在缺陷。