GeoMix

Pytorch implementation of GeoMix: Towards Geometry-Aware Data Augmentation [1].

Environment Set Up

Install the required packages according to requirements.txt.

Tip: Installing torch_geometric with a specific version number may not be easy. I think it is OK to install a version of torch_geometric different from the one I used, as long as it is compatible with your hardware.

Dataset

For the Squirrel and Chameleon datasets, please download the data files from here. Save the Squirrel data file in the ./data/wiki_new/squirrel folder, and save the Chameleon data file in the ./data/wiki_new/chameleon folder.

For the Pileup dataset, please download the following files from this link provided in this GitHub repo: test_gg_PU10.root, test_gg_PU30.root, test_gg_PU50.root, test_qq_PU10.root and test_qq_PU30.root. Save all the files in the ./data/pileup folder.

We provide our extracted features of images from Cifar10 and STL10 datasets in ./data.

For other datasets, our scripts will automatically download data files.

Usage Example

To run GeoMix on Cora dataset, switch to src/main_exp and run the following command:

# GeoMix-I
python main.py --method augTrain --encoder geomix_1 --dataset cora --lr 0.1 \
    --num_layers 2 --hidden_channels 16 --weight_decay 5e-4 --dropout 0.5 \
    --device 1 --alpha 0.1 --hops 3 --label_grad --runs 5 

# GeoMix-II
python main.py --method augTrain --encoder geomix_2 --dataset cora --lr 0.1 \
    --num_layers 2 --hidden_channels 16 --weight_decay 5e-4 --dropout 0.5 \
    --device 1 --hops 2 --alpha 0.1 --label_grad --runs 5

# GeoMix-III

python main.py --method augTrain --encoder geomix_3 --dataset cora --lr 0.1 \
    --num_layers 2 --hidden_channels 16 --weight_decay 5e-4 --dropout 0.5 \
    --hops 2 --res_weight 0.1 --graph_weight 0.8 --use_weight --attn_emb_dim 16 \
    --label_grad --runs 5 --device 3

Please refer to .sh scipts in exp folder in each subdirectory of ./src for experiments on other datasets. Note that you may need to change GPU device ID by adjusting the value of the --device argument.

References

[1] Wentao Zhao, Qitian Wu, Chenxiao Yang, and Junchi Yan. 2024. GeoMix: Towards Geometry-Aware Data Augmentation (KDD '24).

Citation

If you find our code helpful, please consider citing our work

@inproceedings{zhao2023glow,
  title={GeoMix: Towards Geometry-Aware Data Augmentation},
  author={Zhao, Wentao and Wu, Qitian and Yang, Chenxiao and Yan, Junchi}
  booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year={2024}
}