MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation

• URL: http://arxiv.org/abs/2307.07832v1
• Date: Sat, 15 Jul 2023 15:46:38 GMT
• Title: MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation
• Authors: Jiaxing Zhang, Dongsheng Luo, and Hua Wei
• Abstract summary: Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. Post-hoc instance-level explanation methods have been proposed to understand GNN predictions. We shed light on the existence of the distribution shifting issue in existing methods, which affects explanation quality.
• Score: 6.307753856507624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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