Graph Neural Networks (GNNs) have become a building block in graph data
processing, with wide applications in critical domains. The growing needs to
deploy GNNs in high-stakes applications necessitate explainability for users in
the decision-making processes. A popular paradigm for the explainability of
GNNs is to identify explainable subgraphs by comparing their labels with the
ones of original graphs. This task is challenging due to the substantial
distributional shift from the original graphs in the training set to the set of
explainable subgraphs, which prevents accurate prediction of labels with the
subgraphs. To address it, in this paper, we propose a novel method that
generates proxy graphs for explainable subgraphs that are in the distribution
of training data. We introduce a parametric method that employs graph
generators to produce proxy graphs. A new training objective based on
information theory is designed to ensure that proxy graphs not only adhere to
the distribution of training data but also preserve essential explanatory
factors. Such generated proxy graphs can be reliably used for approximating the
predictions of the true labels of explainable subgraphs. Empirical evaluations
across various datasets demonstrate our method achieves more accurate
explanations for GNNs.

我们提出了一种使用代理图生成可解释子图的新方法，通过信息论设计的训练目标确保代理图符合训练数据的分布并保留基本的解释因素，从而实现更准确的图神经网络解释。