Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predictions. However, it is not clear yet how to evaluate the correctness of those explanations, whether it is from a human or a model perspective. One unaddressed bottleneck in the current evaluation procedure is the problem of out-of-distribution explanations, whose distribution differs from those of the training data. This important issue affects existing evaluation metrics such as the popular faithfulness or fidelity score. In this paper, we show the limitations of faithfulness metrics. We propose GInX-Eval (Graph In-distribution eXplanation Evaluation), an evaluation procedure of graph explanations that overcomes the pitfalls of faithfulness and offers new insights on explainability methods. Using a retraining strategy, the GInX score measures how informative removed edges are for the model and the EdgeRank score evaluates if explanatory edges are correctly ordered by their importance. GInX-Eval verifies if ground-truth explanations are instructive to the GNN model. In addition, it shows that many popular methods, including gradient-based methods, produce explanations that are not better than a random designation of edges as important subgraphs, challenging the findings of current works in the area. Results with GInX-Eval are consistent across multiple datasets and align with human evaluation.

翻译：近年来，研究者们开发了多种图神经网络（GNN）的解释方法，旨在突出图中对模型预测贡献最大的边和节点。然而，无论从人类视角还是模型视角，如何评估这些解释的正确性仍不明确。当前评估流程中一个尚未解决的瓶颈在于分布外解释问题，即这些解释的分布与训练数据分布存在差异。这一重要缺陷影响了现有的评估指标（如流行的忠实度或保真度评分）。本文揭示了忠实度指标的局限性，并提出GInX-Eval（图分布内解释评估）——一种克服忠实度缺陷并提供解释方法新视角的图解释评估流程。通过重训练策略，GInX评分衡量被移除边对模型的信息贡献程度，EdgeRank评分则评估解释性边是否按其重要性正确排序。GInX-Eval可验证真实解释是否对GNN模型具有指导意义。此外，实验表明，包括基于梯度方法在内的多种主流方法产生的解释效果并不优于随机指定重要子图的边，这挑战了当前领域的研究结论。基于GInX-Eval的结果在多个数据集上保持一致，并与人工评估结果吻合。