Pick perturbation with lowest score about gnn-meta-attack HOT 6 CLOSED

Hi,

Yes, that should do the job. Good luck and let me know if you have further questions!

Daniel

from gnn-meta-attack.

Thanks!

I actually do have a couple of other questions:

• Can the algorithm perturb a selfloop?
• Is it possible for the algorithm to pick the same edge more than once?
• How should I read the content of self.adjacency_meta_update after the graph has been perturbed?

Let me know if you want me to open a separate issue for this or reach you some other way.
Thanks again!

from gnn-meta-attack.

Can the algorithm perturb a selfloop?

No, since the self-loop is being added in a "hard-coded" way by the GCN preprocessing. However you can change that in the code if you like.

Is it possible for the algorithm to pick the same edge more than once?

Technically that's possible, though unlikely, I guess. You could set the meta-gradients of the entries that were previously selected to 0 if you want that.

How should I read the content of self.adjacency_meta_update after the graph has been perturbed?

I'm honestly not sure whether there is anything to read from self.adjacency_meta_update. If you want the perturbed edges, you can look at the nonzero entries in self.adjacency_changes.

Does that help?

from gnn-meta-attack.

Technically that's possible, though unlikely, I guess. You could set the meta-gradients of the entries that were previously selected to 0 if you want that.

I don't really want to perturb the same edge twice, just checking if it's possible because I suspect it might happening to me 😅

I'm honestly not sure whether there is anything to read from self.adjacency_meta_update. If you want the perturbed edges, you can look at the nonzero entries in self.adjacency_changes.

Isn't self.adjacency_meta_update supposed to have the updated content of self.adjacency_changes?

# Add the change to the perturbations.
self.adjacency_meta_update = tf.scatter_add(self.adjacency_changes,
                                            indices=adj_argmax_combined,
                                            updates=-2 * tf.gather(
                                                 tf.reshape(self.modified_adjacency, [-1]),
                                                 adj_argmax_combined) + 1)

How do I interpret self.adjacency_changes? A 0 if the edge hasn't been perturbed and a 1 if it has (regardless if the edge has been 'added' or 'removed') ?
What if an edge has been perturbed twice? Will the entry in self.adjacency_changes be a 1 or a 0 (because it went from 0 to 1 and from 1 to 0) ?

Does that help?

Yes! Thanks!

from gnn-meta-attack.

Hi,

as far as I remember, tf.scatter_add is the operation that directly modifies the target tensor, i.e. in this case self.adjacency_changes. self.adjacency_changes is a [N*N] tensor that contains 1 for edge insertions, -1 for edge deletions, and 0 else. This is then added to the original adjacency matrix to obtain the perturbed adjacency matrix. If an edge is modified twice, it is set back to its original state. If you don't want that you can manually set to zero the gradients corresponding to the already modified indices.

I hope that helps.

Best,

Daniel

from gnn-meta-attack.

That really helps! Thank you very much!

from gnn-meta-attack.