Han Rohe 牛宇韬

[n1108]

Robust Heterogeneous Graph Neural Network (RoHeHAN)

This is an implementation of RoHeHAN, a robust heterogeneous graph neural network designed to defend against adversarial attacks on heterogeneous graphs.

• Paper link: https://cdn.aaai.org/ojs/20357/20357-13-24370-1-2-20220628.pdf
• Original paper title: Robust Heterogeneous Graph Neural Networks against Adversarial Attacks
• Implemented using tensorlayerx and gammagl libraries.

Usage

To reproduce the RoHeHAN results on the ACM dataset, run the following command:

TL_BACKEND="torch" python rohehan_trainer.py --num_epochs 100 --gpu 0

Performance

Reference performance numbers for the ACM dataset:

Dataset	Clean (no attack)	Attack(1 perturbation)	Attack(3 perturbations)	Attack(5 perturbations)
ACM	0.930	0.915	0.905	0.895

ACM dataset link: https://github.com/Jhy1993/HAN/raw/master/data/acm/ACM.mat

Example Commands

You can adjust training settings, such as the number of epochs, learning rate, and dropout rate, with the following commands:

TL_BACKEND="torch" python rohehan_trainer.py --num_epochs 200 --lr 0.005 --dropout 0.6 --gpu 0

Notes

• Early stopping is used to prevent overfitting during training.
• The settings in the RoHeGAT layer control the attention purifier mechanism, which ensures robustness against adversarial attacks by pruning unreliable neighbors.

This implementation builds on the idea of using metapath-based transiting probability and attention purification to improve the robustness of heterogeneous graph neural networks (HGNNs).