Code of the AAAI 2021 paper HGCN --- Supplementary
Option 1: Nvidia Docker image
docker pull utasmile/pytorch:1.4.0-cuda10.0-cudnn7.5-devel
Option 2: Install the dependencies by yourself
- Pytorch >= 1.4.0
- Pytorch Geometric
All the datasets used in the paper can be downloaded from Benchmark Data Sets for Graph Kernels
All the commands can be found in script.txt
For example:
python main.py --epochs 100 --batch_size 256 --capsule-dimensions 128 --capsule-num 10 --dropout 0.1 --theta 0.1 --lr 0.003 --dataset IMDB-BINARY
If you use HGCN in your research, please cite the following paper:
@article{yang2020hierarchical,
title={Hierarchical Graph Capsule Network},
author={Yang, Jinyu and Zhao, Peilin and Rong, Yu and Yan, Chaochao and Li, Chunyuan and Ma, Hehuan and Huang, Junzhou},
journal={AAAI Conference on Artificial Intelligence (AAAI)},
year={2021}
}
The code is largely borrowed from:
@article{chen2019powerful,
title={Are powerful graph neural nets necessary? a dissection on graph classification},
author={Chen, Ting and Bian, Song and Sun, Yizhou},
journal={arXiv preprint arXiv:1905.04579},
year={2019}
}