| PyTorch Implementation of Graph U-Nets | |
| ====================================== | |
| Created by [Hongyang Gao](https://faculty.sites.iastate.edu/hygao/) @ Iowa State University, and | |
| [Shuiwang Ji](http://people.tamu.edu/~sji/) @ Texas A&M University. | |
| About | |
| ----- | |
| PyTorch implementation of Graph U-Nets. Check http://proceedings.mlr.press/v97/gao19a/gao19a.pdf for more information. | |
| Methods | |
| ------- | |
| ### Graph Pooling Layer | |
|  | |
| ### Graph Unpooling Layer | |
|  | |
| ### Graph U-Net | |
|  | |
| Installation | |
| ------------ | |
| 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. | |
| Code | |
| ---- | |
| The detail implementation of Graph U-Net is in src/utils/ops.py. | |
| Datasets | |
| -------- | |
| Check the "data/README.md" for the format. | |
| Results | |
| ------- | |
| | 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%** | | |
| Reference | |
| --------- | |
| 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} | |
| } | |