MixupExplainer: Generalizing Explanations for Graph Neural Networks with
  Data Augmentation
Jiaxing Zhang Dongsheng Luo Hua Wei
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.