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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Implicit Manifold-valued Diffusions (IMDs) are data-driven SDEs built from proximity graphs that converge in law to smooth manifold diffusions as sample count increases.
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
Implicit Manifold-valued Diffusions (IMDs) are data-driven SDEs built from proximity graphs that converge in law to smooth manifold diffusions as sample count increases.