You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
, where it drops the edge based on the similarity score in Equation 3 of the paper. However, the paper only mentions that dropping the edge is based on the Equation 5, not Equation 3. Could you kindly explain why do we add this line in the code? Or something I'm missing?
Thanks!
The text was updated successfully, but these errors were encountered:
, where it drops the edge based on the similarity score in Equation 3 of the paper. However, the paper only mentions that dropping the edge is based on the Equation 5, not Equation 3. Could you kindly explain why do we add this line in the code? Or something I'm missing?
Thanks!
, where it drops the edge based on the similarity score in Equation 3 of the paper. However, the paper only mentions that dropping the edge is based on the Equation 5, not Equation 3. Could you kindly explain why do we add this line in the code? Or something I'm missing?
Thanks!
As indicated in "Are Defenses for Graph Neural Networks Robust?", It is vital to do this adjustment to remove edges with too dissimilar node embeddings (which would, as experiments show, substantially hinder attacking).
Paper link: https://www.cs.cit.tum.de/daml/are-gnn-defenses-robust/
Hi,
Thanks for the great work! I have a question for the code
GNNGuard/defense/gcn.py
Line 184 in 33f5390
Thanks!
The text was updated successfully, but these errors were encountered: