Faithful, Sufficient and Understandable: Rethinking Graph Counterfactual Explanations via Discrete Diffusion Inversion
Graph Neural Networks (GNNs) achieve strong predictive performance on graph-structured data across domains such as chemistry, biology, and network analysis, yet they provide no intrinsic explanation of their predictions. This limits their adoption in high-stakes and safety-critical settings. Counterfactual explanations address this by revealing the minimal structural modifications that would change a model's prediction. On graphs, however, such a modification is hard to produce. The search space
Record details
Published: 12 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Biotech
Retrieved: 13 August 2026
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ethics.ai (12 August 2026), “Faithful, Sufficient and Understandable: Rethinking Graph Counterfactual Explanations via Discrete Diffusion Inversion,” evidence record 19038, https://ethics.ai/record/19038 (originally published by arXiv cs.AI).
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