cornell-zhang / garnet Goto Github PK
View Code? Open in Web Editor NEWGARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
License: BSD 3-Clause "New" or "Revised" License
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
License: BSD 3-Clause "New" or "Revised" License
Could you please provide the hyperparameters to run GARNET on arxiv
and products
?
Thanks for your great work!
In Definition 3.1,
Given the top r smallest eigenvalues λ1, λ2, ..., λr and their corresponding eigenvectors
you are using the smallest eigenvalues building the weighted spectral embedding matrix V, while in the next part,due to the dominant singular components are hardly affected by adversarial attacks, the weighted spectral embedding is therefore also resistant to adversarial attacks,so it's safe to use the adv's weighted spectral embedding matrix V.
In the adversarial attacks,it will not change the dominant singular components ,but only the small singular components are getting changed? Does it means right? So why the V stay unchanged for using small eigenvalues ? because 1- λ1?
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