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WOMBAT benchmark tests GNN explainers on molecular data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed WOMBAT, a new benchmark designed to test the accuracy of explainability tools for Graph Neural Networks (GNNs) when applied to molecular data. WOMBAT consists of 14 GNNs with manually defined decision rules, allowing for ground truth attribution analysis. This benchmark was validated on millions of PubChem molecules and used to evaluate existing explainers like GNNExplainer, PGExplainer, and Integrated Gradients, revealing specific failure modes. AI

IMPACT Improves evaluation of AI explainability tools for molecular graphs, potentially leading to more reliable AI safety and attribution methods.

RANK_REASON The cluster describes a new benchmark and dataset for evaluating AI research tools, specifically for GNN explainability. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

WOMBAT benchmark tests GNN explainers on molecular data

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The cluster describes a new benchmark and dataset for evaluating AI research tools, specifically for GNN explainability. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Dominik Matuszek, Bartosz Zieli\'nski, Tomasz Danel, Dawid Rymarczyk ·

    WOMBAT: Whitebox Oracle for Molecular Benchmarking and Attribution Testing

    arXiv:2610.00713v1 Announce Type: new 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…

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