- PyTorch 0.4 or 0.5
- Python 2.7 or 3.6
- networkx, scipy, sklearn, numpy, pickle
Example: python generate_perturbation.py --dataset cora --radius 4
dataset: the network dataset you are going to attack
radius: the radius of the l2 Norm Projection
The verision of jupyter notebook is also supported as: universal_attack.ipynb
After finishing the training of the GUA, we then evaluate the test asr over the test nodes
Example: python eval_baseline.py --dataset cora --radius 4 --evaluate_mode universal
dataset: the network dataset you are going to attack
radius: the radius of the l2 Norm Projection
evaluate_mode has five values:
- "universal": graph universal attack
- "limitted_attack": random attack, a prescribed number of anchor nodes are randomly sampled
- "global_random": global random attack
- "victim_attack": victim attack
- "universal_delete": randomly delete a part of nodes from the trained anchor nodes, to find the trade-off
- "advanced_victim_attack": a prescribed number of anchor nodes are composed of the nodes with the highest confidence from the victim class
- "advanced_limitted_attack": advanced random attack, a prescribed number of anchor nodes are randomly sampled from the top 10% nodes with the highest degrees
The perturbation results trained by GUA when radius = 4, for each dataset: Cora, Citeseer and Pol.Blogs are also provided in "GUA/perturbation_results", which can be used directly for testing
The verision of jupyter notebook is also supported as: evaluate.ipynb
You can also validate the transferability on other embedding methods:
Node2vec: python node2vec/evaluate_n2v --dataset cora
DeepWalk: python deepwalk/evaluate_deepwalk --dataset cora
pyGAT: python pyGAT/evaluate_GAT --dataset cora