Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions

This codebase has been used to generate the results found in the NeurIPS 2023 paper Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions.

Installation

The code requires the following packages and has been tested with the given versions:

python 3.9.7
pytorch 1.10.2
cudatoolkit 11.3.1
torchvision 0.11.3
pyg 2.0.3
sacred 0.8.2
tqdm 4.62.3
scipy 1.7.3
torchtyping 0.1.4
seml 0.3.6
numba 0.54.1
filelock 3.4.2
numpy 1.20.3
scikit-learn 1.0.2
tinydb 4.6.1
tinydb-serialization 2.1.0
tqdm 4.62.3
ogb 1.3.2
torchtyping 0.1.4
cvxpy 1.2.1

For the experiments done using Soft-Median or Randomized Smoothing, additional packages are necessary and explained below, in their respective sections.

Main Package

Thereafter one can install the actual module via (alternatively use python install .):

pip install .

Structure

Besides the standard python artifacts we provide:

• cache: for the pretrained models / attacked adjacency matrices
• config: the configuration files grouped by experiments
• data: for storing the datasets
• experiments: source code defining the types of experiments
• kernels: the custom kernel package (only relevant for soft-median)
• log: for logging the output of experiments
• robust_diffusion: the source code

Experiments

All experiments use seml. For an introduction into seml, we refer to the official examples on the seml github repository.

By default all the results of the experiments will be logged into ./log.

Adversarial Training

Inductive Case

The experiment code can be found in experiments/experiment_adv_train_inductive.py. The corresponding seml experiment configuration files can be found in config/train under the name of adv_ind_lrbcd.yaml for LR-BCD and adv_ind_prbcd.yaml for PR-BCD.

Exemplary, adversarial training with the LR-BCD configuration file can be performed by:

seml [mongodb-collection-name] add config/train/adv_ind_lrbcd.yaml start

Optionally, the experiments can be run locally by adding the --local flag:

seml [mongodb-collection-name] add config/train/adv_ind_lrbcd.yaml start --local

Transductive Case

The experiment code for adversarial training with PGD in the transductive case can be found in experiments/experiment_adv_train_transductive_pgd.py. The respectective experiment configuration files can be found in config/train under adv_trans_pgd.yaml for moderate adversaries and for adv_trans_pgd_perfect_rob.yaml for a strong adversary, where GPRGNN achieves perfect robustness.

Robustness Evaluation

The experiment code for evaluating the robustness of trained models can be found in experiments/experiment_global_attack_direct.py. The corresponding experiment configuration files are in config/attack_evasion_global_direct and prefixed lrbcd_ for LR-BCD adversarial training, prbcd_ for PR-BCD adversarial training, and pgd_ for (transductive) PGD adversarial training.

For evaluation, we use the locally stored models in the cache folder (unless specified differently).

Certifiable Robustness

Requires the following additional packages:

https://github.com/abojchevski/sparse_smoothing.git
gmpy2
statsmodels

The experiment code for certifying accuracy using sparse smoothing can be found in experiments/experiment_sparse_smoothing.py and the relevant configuration files in config/sparse_smoothing.

Soft Median

For using the soft median defense, custom cuda kernels found in kernels have to be installed. For the instructions, we refer to Robustness of Graph Neural Networks at Scale. The corresponding experiment config file is config/train/ind_soft_median.yaml.

Cite

Please cite our paper if you use this code in your own work:

@inproceedings{
    gosch2023adversarial,
    title={Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions},
    author={Lukas Gosch and Simon Geisler and Daniel Sturm and Bertrand Charpentier and Daniel Z{\"u}gner and Stephan G{\"u}nnemann},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)},
    year={2023},
 }

Contact

For questions and feedback, please do not hesitate to contact:

Lukas Gosch, l (dot) gosch (at) tum (dot) de, Technical University of Munich
Simon Geisler, s (dot) geisler (at) tum (dot) de, Technical University of Munich

Other Notes and Credit

This codebase has been developed out of a fork of

• Robustness of Graph Neural Networks at Scale

and adapted for this project. As a result, there is a significant overlap of code. We thank the authors for making their code public and the development team of PyTorch Geometric as well as seml.