Contrastive Loss Gradient Attack (CLGA)

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Deep Learning CLGA
Overview

Contrastive Loss Gradient Attack (CLGA)

Official implementation of Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation, WWW22

Built based on GCA and DeepRobust.

Requirements

Tested on pytorch 1.7.1 and torch_geometric 1.6.3.

Usage

1.To produce poisoned graphs with CLGA

python CLGA.py --dataset Cora --num_epochs 3000 --device cuda:0

It will automatically save three poisoned adjacency matrices in ./poisoned_adj which have 1%/5%/10% edges perturbed respectively. You may reduce the number of epochs for a faster training.

2.To produce poisoned graphs with baseline attack methods

python baseline_attacks.py --dataset Cora --method dice --rate 0.10 --device cuda:0

It will save one poisoned adjacency matrix in ./poisoned_adj.

3.To train the graph contrastive model with the poisoned graph

python train_GCA.py --dataset Cora --perturb --attack_method CLGA --attack_rate 0.10 --device cuda:0

It will load and train on the corresponding poisoned adjacency matrix specified by dataset, attack_method and attack_rate.

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Comments
  • 运行时出错

    运行时出错

    作者你好： 我在运行CLGA.py时出现如下错误，我应该如何解决，期待你的回答！ Perturbing edges: 50/527. Finished in 8.24s Perturbing edges: 51/527. Finished in 8.91s Perturbing edges: 52/527. Finished in 9.18s Traceback (most recent call last): File "C:\Users\zhangqi\Desktop\CLGA-main\CLGA.py", line 243, in poisoned_adj = model.attack() File "C:\Users\zhangqi\Desktop\CLGA-main\CLGA.py", line 147, in attack pkl.dump(output_adj.to(torch.device('cpu')), open('poisoned_adj/%s_CLGA_0.010000_adj.pkl' % args.dataset, 'wb')) FileNotFoundError: [Errno 2] No such file or directory: 'poisoned_adj/Cora_CLGA_0.010000_adj.pkl'

    opened by xiangxinchufa 1
  • about poisoned graph in polblogs

    about poisoned graph in polblogs

    Hi, RinneSz

    I've already run your code,but i don't know how to generate poisoned graphs in the dataset polblogs(Cora and CiteSeer is successful).How can I do to generate it?

    opened by lizehaodashuaibi 5
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