Accepted by AAAI 2022

In recent years, plentiful evidence illustrates that Graph Convolutional Networks (GCNs) achieve extraordinary accomplishments on the node classification task. However, GCNs may be vulnerable to adversarial attacks on label-scarce dynamic graphs. Many existing works aim to strengthen the robustness of GCNs; for instance, adversarial training is used to shield GCNs against malicious perturbations. However, these works fail on dynamic graphs for which label scarcity is a pressing issue. To overcome label scarcity, self-training attempts to iteratively assign pseudo-labels to highly confident unlabeled nodes but such attempts may suffer serious degradation under dynamic graph perturbations. In this paper, we generalize noisy supervision as a kind of self-supervised learning method and then propose a novel Bayesian self-supervision model, namely GraphSS, to address the issue. Extensive experiments demonstrate that GraphSS can not only affirmatively alert the perturbations on dynamic graphs but also effectively recover the prediction of a node classifier when the graph is under such perturbations. These two advantages prove to be generalized over three classic GCNs across five public graph datasets.

cora, citeseer, pubmed, amazoncobuy, coauthor

Linux or macOS
CPU or NVIDIA GPU + CUDA CuDNN
Python 3
pytorch, dgl, numpy, scipy, sklearn, numba

git clone https://github.com/junzhuang-code/GraphSS.git
cd GraphSS/graphSS

For pip users, please type the command: pip install -r requirements.txt
For Conda users, you may create a new Conda environment using: conda env create -f environment.yml

1. pretrain.py: Train the node classifier on the train graph
2. attack.py: Implement adversarial attacks
3. main.py: Infer labels by GraphSS
4. models_GCN.py/models_SS.py: model scripts
5. load_data.py: Load data script
6. utils.py: Utils modules

1. attacker_data: a directory that stores attacked graph and GCN's weights
2. noisy_label: a directory that stores labels

1. Train the node classifier on the train graph
   python [script_name] -data_name -model_name -NUM_EPOCHS -GPU -NOISY_RATIO -is_trainable
   e.g. python pretrain.py cora GCN 200 0 0.1 true

2. Apply adversarial attacks and generate predicted labels
   python [script_name] -data_name -model_name -TARGET_CLASS -attack_type -NUM_PERT -SAMPLE_RATE -GPU -NOISY_RATIO -is_attack
   e.g. python attack.py cora GCN -1 "lf" 2 0.2 0 0.1 true

3. Inference and evaluation
   python [script_name] -data_name -model_name -is_attacked -TARGET_CLASS -attack_type -SAMPLE_RATE -GPU -is_inferable -ssup_type -NUM_RETRAIN -NUM_INFER -WARMUP_STEP -Alert -Dynamic_Phi
   e.g. python main.py cora GCN true -1 "lf" 0.1 0 true "pseudo" 60 100 40 "" true

4. Visualization with Tensorboard
   Under the directory of "GraphSS", run: Tensorboard --logdir=./runs/Logs_SS --port=8999

AdvTrain: When Does Self-Supervision Help Graph Convolutional Networks? [Code]
GNN-Jaccard: Adversarial Examples on Graph Data: Deep Insights into Attack and Defense [Code]
GNN-SVD: All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs [Code]
RGCN: Robust Graph Convolutional Networks Against Adversarial Attacks [Code]
GRAND: Graph Random Neural Network for Semi-Supervised Learning on Graphs [Code]
ProGNN: Graph Structure Learning for Robust Graph Neural Networks [Code]
NRGNN: NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs [Code]

Please cite our paper if you think this repo is helpful.

@article{zhuang2022defending,
  author={Zhuang, Jun and Al Hasan, Mohammad},
  year={2022},
  month={Jun.},
  title={Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-Supervision},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={4},
  pages={4405-4413},
  DOI={10.1609/aaai.v36i4.20362},
  url={https://ojs.aaai.org/index.php/AAAI/article/view/20362}
}