File size: 1,704 Bytes
bf620c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
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}
}
|