HongyangGao / Graph-U-Nets

Pytorch implementation of Graph U-Nets (ICML19)

PyTorch Implementation of Graph U-Nets

Created by Hongyang Gao @ Iowa State University, and Shuiwang Ji @ 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}
}