MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation
This work addresses interpretability challenges for GNNs in applications like social networks or biology, but it is incremental as it builds on existing Graph Information Bottleneck methods.
The authors tackled the distribution shifting issue in existing post-hoc explanation methods for Graph Neural Networks (GNNs), which affects explanation quality, especially in real-life datasets with tight decision boundaries, and proposed MixupExplainer, a graph mixup method with theoretical guarantees that showed effectiveness in experiments on synthetic and real-world datasets.
Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. However, their predictions are often not interpretable. 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 shed light on the existence of the distribution shifting issue in existing methods, which affects explanation quality, particularly in applications on real-life datasets with tight decision boundaries. To address this issue, we introduce a generalized Graph Information Bottleneck (GIB) form that includes a label-independent graph variable, which is equivalent to the vanilla GIB. Driven by the generalized GIB, we propose a graph mixup method, MixupExplainer, with a theoretical guarantee to resolve the distribution shifting issue. We conduct extensive experiments on both synthetic and real-world datasets to validate the effectiveness of our proposed mixup approach over existing approaches. We also provide a detailed analysis of how our proposed approach alleviates the distribution shifting issue.