NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption
Noise Corruption is introduced, a Noise Corruption-based explanation framework, which perturbs each message through matched-norm random-direction corruption while preserving the expected squared message norm, and NICE, a Noise Corruption-based explanation framework, which learns a Stochastic Restoration Boundary under NC-induced uncertainty, balancing target-prediction restoration against compactness.
Ziluowen Luo, Jun Yin, Ruochen Liu et al.
· 0 citations