论文标题
L2XGNN：学习解释图形神经网络
L2XGNN: Learning to Explain Graph Neural Networks
论文作者
论文摘要
图神经网络（GNN）是一类流行的机器学习模型。受到学习解释（L2X）范式的启发，我们提出了L2XGNN，这是一个可解释的GNNS的框架，可以通过设计提供忠实的解释。 L2XGNN学习了一种选择解释性子图（主题）的机制，该机制仅在GNNS消息传播操作中使用。 L2XGNN能够为每个输入图选择具有特定属性的子图，例如稀疏和连接。对图案施加这种限制通常会导致更容易解释和有效的解释。几个数据集的实验表明，L2XGNN使用整个输入图实现了与基线方法相同的分类精度，同时确保仅使用提供的解释来做出预测。此外，我们表明L2XGNN能够识别负责预测图形属性的主题。
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.
