GOOD-AT

This repo is for source code of ICLR 2024 paper "BOOSTING THE ADVERSARIAL ROBUSTNESS OF GRAPH NEURAL NETWORKS: AN OOD PERSPECTIVE".

Paper Link: https://openreview.net/forum?id=DCDT918ZkI&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DICLR.cc%2F2024%2FConference%2FAuthors%23your-submissions)

Environment

• python == 3.8.8
• pytorch == 2.0.0+cu118
• scipy == 1.6.2
• numpy == 1.20.1
• deeprobust

Test GOOD-AT on Perturbed Graphs

We provide an example of how to test GOOD-AT on a perturbed graph. We choose cora with 415 perturbations, which is generated from PGD in the unit test. If you want to conduct experiments on your own perturbed graphs, you just need to modify the dataset load module in this code. You can simply run

python good.py

Unit Test

Adversarial Unit Test is proposed by Mujkanovic et al. (2022)[https://www.cs.cit.tum.de/daml/are-gnn-defenses-robust/] which consists of seven adaptive attack methods. Referring to (Günnemann, 2022), they categorize the attack methods into seven classes and select the most representative methods from each class as the targets of adaptive attacks. The adversarial graphs generated from these attacks can be encapsulated together to test other defenses, and it can be regarded as the minimum criterion for assessing the adaptive robustness of the defense models.

The unit_test module used in this script is adapted from the following repository: https://github.com/LoadingByte/are-gnn-defenses-robust

The perturbed_graph/unit_test.npz is the direct copy from https://github.com/harishgovardhandamodar/are-gnn-defenses-robust/blob/master/unit_test/unit_test.npz

The gb module used in this script is also obtained from the same repository.

Our usage of the unit_test and gb modules from the referenced repository ensures comparable and scientific results in our experiments. By utilizing established modules from a reputable source, we maintain consistency with existing methodologies and contribute to the reproducibility of scientific findings in the field.

To conduct unit test for baselines, run

python baseline_unit_test.py

For GOOD-AT, run

python goodat_unit_test.py

Hyper-parameters

The hyper-parameters are well set in good.py and goodat_unit_test.py, and we do not modify it under any different perturbation rate.

• K = 20: the number of detectors
• d_epochs = 50: the number of epochs for training the detector
• d_batchsize = 2048 the batch size for training the detector
• ptb_d = 0.3: the perturbation rate for training the detector
• threshold = 0.1: the threshold of detecting adversarial edges

Self-training-based Poisoning defense

For the self-training defense, please refer to our another repository: https://github.com/likuanppd/STRG

Citation

@inproceedings{li2023boosting,
  title={Boosting the Adversarial Robustness of Graph Neural Networks: An OOD Perspective},
  author={Li, Kuan and Chen, YiWen and Liu, Yang and Wang, Jin and He, Qing and Cheng, Minhao and Ao, Xiang},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2023}
}

Contact

If you have any questions, please feel free to contact me with likuan.ppd@gmail.com.