Graph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2XGNN, a framework for explainable GNNs which provides faithful explanations by design. L2XGNN learns a mechanism for selecting explanatory subgraphs (motifs) which are exclusively used in the GNNs message-passing operations. L2XGNN is able to select, for each input graph, a subgraph with specific properties such as being sparse and connected. Imposing such constraints on the motifs often leads to more interpretable and effective explanations. Experiments on several datasets suggest that L2XGNN achieves the same classification accuracy as baseline methods using the entire input graph while ensuring that only the provided explanations are used to make predictions. Moreover, we show that L2XGNN is able to identify motifs responsible for the graph's properties it is intended to predict.

翻译：图神经网络（GNN）是一类流行的机器学习模型。受学习解释（L2X）范式的启发，我们提出了L2XGNN，这是一个为可解释GNN设计的框架，其本质上是忠实的解释。L2XGNN学习了一种选择解释性子图（模体）的机制，这些子图仅用于GNN的消息传递操作。L2XGNN能够针对每个输入图选择具有特定属性（例如稀疏性和连通性）的子图。在模体上施加此类约束通常会导致更具可解释性和更有效的解释。在多个数据集上的实验表明，L2XGNN在使用完整输入图的基线方法中实现了相同的分类准确率，同时确保仅使用所提供的解释进行预测。此外，我们展示了L2XGNN能够识别负责其旨在预测的图属性的模体。