Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. To open the black-box of these deep learning models, post-hoc instance-level explanation methods have been proposed to understand GNN predictions. These methods seek to discover substructures that explain the prediction behavior of a trained GNN. In this paper, we show analytically that for a large class of explanation tasks, conventional approaches, which are based on the principle of graph information bottleneck (GIB), admit trivial solutions that do not align with the notion of explainability. Instead, we argue that a modified GIB principle may be used to avoid the aforementioned trivial solutions. We further introduce a novel factorized explanation model with theoretical performance guarantees. The modified GIB is used to analyze the structural properties of the proposed factorized explainer. We conduct extensive experiments on both synthetic and real-world datasets to validate the effectiveness of our proposed factorized explainer.

翻译：图神经网络（GNNs）因其处理图结构数据的能力而日益受到关注。为揭示这些深度学习模型的黑箱特性，研究者提出了一系列事后实例级解释方法以理解GNN的预测机制。此类方法旨在发现能够解释已训练GNN预测行为的子结构。本文通过理论分析表明，对于大规模解释任务，基于图信息瓶颈（GIB）原理的传统方法存在与可解释性概念相悖的平凡解。我们提出采用修正的GIB原理以避免上述平凡解，并进一步引入一种具有理论性能保证的新型因子化解释模型。该修正GIB被用于分析所提因子化解释器的结构特性。我们在合成数据集与真实数据集上开展大量实验，验证了所提因子化解释器的有效性。