In this repository, we implement the SSFG (stochastic ReLU) regularization method for regularizing graph convoultional networks. Our paper "SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks" was accepted for publication in IEEE Transactions on Neural Networks and Learning Systems, DOI:10.1109/TNNLS.2022.3188888. The paper (arxiv version) can be accessed through this link.

@article{zhang2022ssfg,
  title={SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks},
  author={Haimin Zhang, Min Xu, Guoqiang Zhang, and Kenta Niwa},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2022}
}

Install PyTorch and the benchmark datasets following the official Benchmarking Graph Neural Networks.

Run the following command to reproduce our results on CIFAR10 using GatedGCN for superpixel graph classification:

python main_superpixels_graph_classification.py --dataset CIFAR10 --seed 95 --gpu_id 0 --config configs/superpixels_graph_classification_GatedGCN_CIFAR10_100k.json

For graph attention networks (GATs), the SSFG regularization is applied to the output of each of the multi-attention heads. This can be done by making the following change in layers/myreg.py:

lam = bt.sample(grad_output.shape[:2]) + .5  # change grad_output.shape[:1] to grad_output.shape[:2]

This project is currently under the CC-BY-NC 4.0 license. See LICENSE for details.