This repository is a pytorch implementation of Adversarial Attacks on Graph Neural Networks via Meta Learning.
It is modified from following githubs :
https://github.com/ChandlerBang/pytorch-gnn-meta-attack (Main Reference, Pytorch)
https://github.com/danielzuegner/gnn-meta-attack (Official, Tensorflow)
https://github.com/Kaushalya/gnn-meta-attack-pytorch (Pytorch)
- python 3.6
- torch 1.2
- torchvision 0.5
- higher
- numpy
- scipy
- matpotlib
- seaborn
See help (-h flag) for detailed parameter list of each script before executing the code.
To train the model(s) in the paper, run this command:
# cora_ml
python main.py --train True --hidden 16 --data-name cora_ml --save-path sample.pth
To evaluate the model(s) saved locally, run this command:
# cora_ml
python main.py --train False --hidden 6 --data-name cora_ml --save-path sample.pth
Here is how to generate poisoned data :
# cora_ml
python poison.py --hidden 6 --lambda_ 0.5 --train-iters 15 --perturb-rate 0.05 --save-path sample.pth --data-name cora_ml
(+) Approximated Meta Attack is not implemented.
The accuracy on each dataset :
(+) Unless it's mentioned, all values are set to default. (+) For each method, lambdas are 0(Self), 0.5(Both) and 1(Train).
| Description | Perturb Rate | Accuracy(Dropped) | Reported | Data Name |
|---|---|---|---|---|
| Clean | 0.00 | 85.80% | 83.40% | cora_ml.npz |
| Self | 0.05 | 80.43%(5.37%p) | 75.50%(7.90%p) | cora_ml_self_5.npz |
| Both | 0.05 | 81.42%(4.38%p) | - | cora_ml_both_5.npz |
| Train | 0.05 | 82.52%(3.28%p) | 78.00%(5.40%p) | cora_ml_train_5.npz |
| Self | 0.20 | 58.01%(27.79%p) | - | cora_ml_self_20.npz |
| Both | 0.20 | 67.73%(18.07%p) | - | cora_ml_both_20.npz |
| Train | 0.20 | 80.03%(5.77%p) | - | cora_ml_train_20.npz |
(+) Due to GPU Limitation, I used train-iters=15 when generating poisoned data. Thus, the accuracies of meta-attack likely to be higher than reported ones.
| Description | Perturb Rate | Accuracy(Dropped) | Reported | Data Name |
|---|---|---|---|---|
| Clean | 0.00 | 70.41% | 72.50% | citeseer.npz |
| Self | 0.05 | 64.35%(6.06%p) | 65.40%(7.10%p) | citeseer_self_5.npz |
| Both | 0.05 | 61.08%(9.33%p) | - | citeseer_both_5.npz |
| Train | 0.05 | 68.98%(1.43%p) | 69.70%(2.80%p) | citeseer_train_5.npz |
(+) Due to GPU Limitation, I used train-iters=15 when generating poisoned data. Thus, the accuracies of meta-attack likely to be higher than reported ones.
| Description | Perturb Rate | Accuracy(Dropped) | Reported | Data Name |
|---|---|---|---|---|
| Clean | 0.00 | 93.18% | 93.60% | polblogs.npz |
| Self | 0.05 | 74.36%(18.82%p) | 77.50%(16.10%p) | polblogs_self_5.npz |
| Both | 0.05 | 78.27%(14.91%p) | - | polblogs_both_5.npz |
| Train | 0.05 | 86.09%(7.09%p) | 83.70%(9.90%p) | polblogs_train_5.npz |
(+) Due to GPU Limitation, I used train-iters=15 when generating poisoned data. Thus, the accuracies of meta-attack alikely to be higher than reported ones.
Here is a visualization of changes of edges with Lightning.
Please see Visulaize.ipynb to reproduce these images.
| cora_ml | cora_ml_both_5 |
|---|---|
 |
 |
(+) Statistics
- Deleted Edges: 0
- Created Edges: 798
- Number of Nodes
- Effected Train Nodes: 282
- Effected Test Nodes: 202