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.

采用 GInX-Eval 评估程序，本研究揭示了解释性方法的限制，并提供了新的见解；结果表明，包括基于梯度的方法在内的许多流行方法产生的解释并不优于将边界随机选择为重要子图，这对当前领域的研究成果提出了质疑。