WOMBAT: Whitebox Oracle for Molecular Benchmarking and Attribution Testing
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Computer Science > Machine Learning
Title:WOMBAT: Whitebox Oracle for Molecular Benchmarking and Attribution Testing
Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00713 [cs.LG] |
| (or arXiv:2610.00713v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00713
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Dominik Matuszek [view email][v1] Wed, 30 Sep 2026 21:02:26 UTC (7,164 KB)
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