Enhancing Graph Neural Network Explainers Using a Distribution Shift Consistency-Guided Generator

Abstract

Graph Neural Network (GNN) explainers aim to identify explanatory subgraphs that provide rationales for GNNs’ predictions. Nevertheless, the distribution shift of subgraphs introduces out-of-distribution (OOD) issues into GNNs’ predictions. The OOD issues compromise explainers’ optimization, as the optimization relies on the prediction differences between the original graph and the subgraph. To this end, we propose a plug-and-play framework to adjust the distribution shift of subgraphs during the explainers’ optimization. By introducing a distribution shift consistency objective, we constrain explanatory subgraphs of similar graphs to be consistent, modeling the learning of explanation as a consistency-guided “denoising” process. Additionally, we propose a parameter-sharing generator to act as a “noise-adding” process in each epoch. This generator learns proxy graphs of explanatory subgraphs to adjust the distribution shift, enhancing the explainers’ optimization. We apply the proposed framework to three state-of-the-art explainers and evaluate its performance on four real-world datasets. The results demonstrate that the learned graphs align with the original graphs’ distribution and enhance the performance of explainers in terms of AUC-ROC, Robust Fidelity, and Stability. Furthermore, it significantly outperforms three state-of-the-art distribution shift adjusting algorithms.