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GUARD: Graph Universal Adversarial Defense

PyTorch implementation of the CIKM 2023 paper: "GUARD: Graph Universal Adversarial Defense" [arXiv].

Fig. 1. An illustrative example of graph universal defense. The universal patch p can be applied to an arbitrary node (here v1) to protect it from adversarial targeted attacks by removing adversarial edges (if exist).

Requirements

  • torch==1.9
  • dgl==0.7.0

Datasets

Install graphattack:

cd GraphAttack
pip install -e .

Quick Start

See demo.ipynb

Reproduce results in the paper

run

python evaluate_guard.py

Cite

if you find this repo helpful, please cite our work:

@inproceedings{li2022guard,
  author       = {Jintang Li and
                  Jie Liao and
                  Ruofan Wu and
                  Liang Chen and
                  Zibin Zheng and
                  Jiawang Dan and
                  Changhua Meng and
                  Weiqiang Wang},
  title        = {{GUARD:} Graph Universal Adversarial Defense},
  booktitle    = {{CIKM}},
  pages        = {1198--1207},
  publisher    = {{ACM}},
  year         = {2023}
}

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guard's Issues

关于模型在计算I时使用的图结构

您好,我在阅读您的文章时发现一个问题,当使用clean graph的degree作为模型的输入时,获得的defense效果要比使用attacked graph的degree作为模型输入好上不少,您是否也注意到过这个问题呢,对于这个结果您怎么看,只是想和您探讨一下,期待您的回复!

No module named 'torch_geometric.nn.dense.linear'

Hi, I do not see any requirement to install torch geometric. So when I ran your demo.ipynb, it caused an error

'No module named 'torch_geometric.nn.dense.linear'

What is the version of torch geometric you used for this repo? I'm using torch geometric 1.7.0

Thanks

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