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Graph Neural Network explanations lack canonicality due to input symmetries

By PulseAugur Editorial · [1 sources] ·

A new research paper explores a fundamental issue in explaining Graph Neural Networks (GNNs), specifically how their explanations can be non-canonical due to input symmetries. The study highlights that gradient-based GNN explainers assign identical attribution scores to chemically equivalent atoms, but the final reported explanation, such as the top-k edges, can arbitrarily favor one over the other based on array ordering. This arbitrariness is a structural obstruction, particularly problematic for datasets like Mutagenicity where such symmetries are common. AI

IMPACT Highlights a fundamental limitation in the interpretability of graph neural networks, potentially impacting trust and reliability in their explanations for molecular analysis.

RANK_REASON Academic paper on graph neural networks. [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 →

Graph Neural Network explanations lack canonicality due to input symmetries

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Academic paper on graph neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xin Xu, Siru Tao, Kaizhen Tan ·

    Automorphism-Induced Non-Canonicity in Top-k Explanations of Graph Neural Networks

    arXiv:2607.26344v1 Announce Type: new Abstract: A gradient-based GNN explainer given a molecule with two chemically equivalent nitro groups assigns them attribution scores that are equal to the last bit. It cannot do otherwise: message passing is exactly permutation equivariant, …

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