Self-inconsistency in SI-GNN explanations arises from re-explanation context perturbation; Self-Denoising post-processing calibrates explanations with one forward pass and improves quality.
John Wiley & Sons
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Defines diffusion processes on implicit data manifolds via proximity-graph approximations to the infinitesimal generator and carré-du-champ operator, proves convergence in law to the continuous manifold process, and provides an Euler-Maruyama integrator validated on synthetic and MNIST manifolds.
citing papers explorer
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Why Self-Inconsistency Arises in GNN Explanations and How to Exploit It
Self-inconsistency in SI-GNN explanations arises from re-explanation context perturbation; Self-Denoising post-processing calibrates explanations with one forward pass and improves quality.
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Diffusion Processes on Implicit Manifolds
Defines diffusion processes on implicit data manifolds via proximity-graph approximations to the infinitesimal generator and carré-du-champ operator, proves convergence in law to the continuous manifold process, and provides an Euler-Maruyama integrator validated on synthetic and MNIST manifolds.