Created by Zahra Dehghanian @ Iowa State University,Saeed Saravani @ Iowa State University, and Alireza Amouzad @ Amirkabir University of Technology.
PyTorch implementation of GIUNets. Check http://proceedings.mlr.press/v97/gao19a/gao19a.pdf for more information.
Type
./run_GNN.sh DATA FOLD GPU
to run on dataset using fold number (1-10).
You can run
./run_GNN.sh DD 0 0
to run on DD dataset with 10-fold cross validation on GPU #0.
The detail implementation of GIUNet is in src/utils/ops.py.
Check the "data/README.md" for the format.
| Models | DD | IMDBMULTI | PROTEINS |
|---|---|---|---|
| PSCN | 76.3 ± 2.6% | 45.2 ± 2.8% | 75.9 ± 2.8% |
| DIFFPOOL | 80.6% | - | 76.3% |
| SAGPool | 76.5% | - | 71.9% |
| GIN | 82.0 ± 2.7% | 52.3 ± 2.8% | 76.2 ± 2.8% |
| g-U-Net | 83.0 ± 2.2% | 56.7 ± 2.9% | 78.7 ± 4.2% |
If you find the code useful, please cite our paper:
@inproceedings{gao2019graph,
title={Graph U-Nets},
author={Gao, Hongyang and Ji, Shuiwang},
booktitle={International Conference on Machine Learning},
pages={2083--2092},
year={2019}
}