发表机构
Jagiellonian University; Ardigen SA（雅盖隆大学; Ardigen公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
WOMBAT通过手工设置权重的白盒GNN基准，提供归因真值，用于识别和评估GNN解释器的错误，并展示了如何使Integrated Gradients产生分散归因。
AI 中文摘要
当图神经网络（GNN）解释器在分子上产生意外的归因时，仅凭归因无法揭示是解释器失败还是模型学习了捷径。我们引入了WOMBAT，一个包含14个白盒GNN的基准，每个GNN的消息传递权重由手工设置以检测特定的SMARTS基序。每个模型的决策规则通过构造已知，提供了归因真值，据此可以识别和研究解释器的错误。我们在数百万个PubChem分子上验证了这些模型，并评估了包括GNNExplainer、PGExplainer和Integrated Gradients在内的后验解释器。在我们的定性分析指导下，我们构建了一个模型，该模型使Integrated Gradients将归因分散到整个图上，尽管该模型可靠地检测到预期的基序。我们发布了数据集、模型和评估代码，以帮助研究人员开发用于GNN的更新的XAI工具。
英文摘要
When a graph neural network (GNN) explainer produces an unexpected attribution on a molecule, the attribution alone cannot reveal whether the explainer has failed or the model has learned a shortcut. We introduce WOMBAT, a benchmark of 14 whitebox GNNs, each with message-passing weights set by hand to detect a specific SMARTS motif. Each model's decision rule is known by construction, providing attribution ground truth against which explainer errors can be identified and studied. We validate the models on millions of PubChem molecules and evaluate post-hoc explainers including GNNExplainer, PGExplainer, and Integrated Gradients. Guided by our qualitative analysis, we construct a model that causes Integrated Gradients to spread attribution across the graph, even though the model reliably detects the intended motif. We release the dataset, models, and evaluation code to help researchers in the development of newer XAI tools for GNNs.
