Where Privacy Belongs: Placement Diagnosis and Certified Selection for Private Counterfactual Explanations on Graphs
cs.LG
Submitted: 2026-09-29
Updated: 2026-09-29
Sources
- On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach
- Node-Level Differentially Private Graph Neural Networks
- COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations
- Private Counterfactual Retrieval
- Differentially Private Counterfactuals via Functional Mechanism
- Opacus: User-Friendly Differential Privacy Library in PyTorch
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