Adversarial Attack on Graph Structured Data (https://arxiv.org/abs/1806.02371, to appear in ICML 2018). This repo contains the code, data and results reported in the paper.

First clone the repo recursively, since it depends on another repo (https://github.com/Hanjun-Dai/pytorch_structure2vec):

git clone [email protected]:Hanjun-Dai/graph_adversarial_attack --recursive

Then download the data using the following dropbox link:

https://www.dropbox.com/sh/mu8odkd36x54rl3/AABg8ABiMqwcMEM5qKIY97nla?dl=0

Put everything under the 'dropbox' folder, or create a symbolic link with name 'dropbox':

ln -s /path/to/your/downloaded/files dropbox

Finally the folder structure should look like this:

graph_adversarial_attack (project root)
|__  README.md
|__  code
|__  pytorch_structure2vec
|__  dropbox
|__  |__ data
|    |__ scratch
|......

Optionally, you can use the data generator under code/data_generator to generate the synthetic data.

The current code depends on pytorch 0.3.1, cffi and CUDA 9.1. Please install using the following command (for linux):

pip install http://download.pytorch.org/whl/cu91/torch-0.3.1-cp27-cp27mu-linux_x86_64.whl 
pip install cffi==1.11.2

The c++ code needs to be built first:

cd pytorch_structure2vec/s2v_lib
make
cd code/common
make

If you want to retrain the target model, go to either code/graph_classification or code/node_classification and run the script in train mode. For example:

cd code/graph_classification
./run_er_components.sh -phase train

You can also use the pretrained model that is the same as used in this paper, under the folder dropbox/scratch/results

In this paper, we presented 5 different approaches for attack, under both graph-level classification and node-level classification tasks. The code for attack can be found under code/graph_attack and code/node_attack, respectively.

For example, to use Q-leaning to attack the graph classification method, do the following:

cd code/graph_attack
./run_dqn.sh -phase train

@article{dai2018adversarial,
  title={Adversarial Attack on Graph Structured Data},
  author={Dai, Hanjun and Li, Hui and Tian, Tian and Huang, Xin and Wang, Lin and Zhu, Jun and Song, Le},
  journal={arXiv preprint arXiv:1806.02371},
  year={2018}
}