Out-of-Distribution-Resistant Evaluations for Explanations of Graph Neural Networks.

Explainability in Graph Neural Networks (GNNs) has shown considerable promise in bolstering their trustworthiness, credibility and transparency. Our research delves into the assessment of explainability within GNNs, a pivotal factor for ensuring the reliability of explainability techniques in real-world applications. Existing evaluation metrics, typically involving taking explanatory subgraphs as inputs and measuring output differences, often face out-of-distribution (OOD) challenges. This issue occurs when explanatory subgraphs do not align with real-world data distributions, affecting the reliability of model explanations. With this in mind, in this work, we endeavor to confront this issue by introducing a novel evaluation metric, termed OOD-resistant Adversarial Robustness (OAR). Specifically, our approach is inspired by adversarial robustness, assessing the resilience of explanation subgraphs to attacks. Additionally, we incorporate a sophisticated OOD reweighting mechanism within the evaluation framework to ensure that assessments remain aligned with the original data distribution. Going beyond this, to accommodate a wider range of evaluation tasks, we further devise a counterfactual attack module and complement the perturbed subgraph using the conditional graph diffusion model. The refined paradigm, termed $ \rm{OAR}^+$, ensures that our metric is versatile and applicable across various contexts. Furthermore, we establish a standardized framework, which serves as a benchmark for evaluating the fairness and accuracy of different metrics. We conduct extensive experiments to validate the effectiveness of the OAR and $ \rm{OAR}^+$. Code is available at https://github.com/MangoKiller/OAR.