Comments (2)
Hi,
thanks for your suggestions. The first question you have to answer is how to use GCN for graph classification. I don't think there's an established way to do that, but the version with the virtual node could work.
Regarding attacking multiple nodes simultaneously: that is definitely possible but will require some coding. The main question I see is how to decide which perturbation to perform: I guess a reasonable starting point would be to always perform the perturbation that yields the largest increase in loss over the whole set of nodes under attack. If these sets of nodes are to become relatively large, say more than 5 or 10 nodes, I would refer you to our latest work in which we basically attack all nodes in the graph simultaneously. You could modify the code to attack, say, a certain 10% of the nodes that you are interested in.
Let me know if I can further assist you.
Daniel
from nettack.
Thanks for your insightful comments. Close the issue for now.
from nettack.
Related Issues (16)
- Dimension of Citeseer Dataset
- a problem about the node feature HOT 1
- About the pytorch version HOT 1
- Computation of score functions in feature attacks HOT 4
- Question about deriving Eq.(17) HOT 2
- Poster & Presentation Slides link is invalid HOT 1
- Question about surrogate model HOT 2
- How to perform structure perturbates on Polblogs?
- AttributeError: A1 not found in demo when perturb_structure = False HOT 1
- No Features for polblogs? HOT 3
- Regeneration of results from the paper HOT 5
- Indirect attack not working when influencers > num_neighbors
- why the model predict the correct label successfully after attack in demo HOT 1
- Have you tried transfering the attack generated by nettack to GAT? HOT 9
- Will the default execution of the code exactly reproduce the example image? HOT 2
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