Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias. (arXiv:2304.00010v1 [cs.LG])

It has become cognitive inertia to employ cross-entropy loss function in
classification related tasks. In the untargeted attacks on graph structure, the
gradients derived from the attack objective are the attacker's basis for
evaluating a perturbation scheme. Previous methods use negative cross-entropy
loss as the attack objective in attacking node-level classification models.
However, the suitability of the cross-entropy function for constructing the
untargeted attack objective has yet been discussed in previous works. This
paper argues about the previous unreasonable attack …

attack attacks budget classification entropy function loss node perspective