Created by Hongyang Gao, and Shuiwang Ji at Texas A&M University.
PyTorch implementation of Graph U-Nets. Check https://arxiv.org/abs/1905.05178 for more information.
The implementation is based on the pytorch version of DGCNN.
unzip pytorch_structure2vec-master.zip
Then, under the "pytorch_structure2vec-master/s2vlib/" directory, type
make -j4
to build the necessary c++ backend.
Type
./run_GUNet.sh DATA FOLD
to run on dataset using fold number (1-10). You can run ./run_GUNet.sh DD 0 to run on DD dataset with 10-fold cross validation.
The detail implementation is in ops.py
Check the "data/README.md" for the format.
If you find the code useful, please cite our paper:
@inproceedings{gao2019graph,
title={Graph {U-nets}},
author={Gao, Hongyang and Ji, Shuiwang},
booktitle={Proceedings of The 36th International Conference on Machine Learning},
year={2019},
}