dmlc--dgl
Mufei Li
56 行
1.7 KiB
Markdown
56 行
1.7 KiB
Markdown
Graph Attention Networks (GAT)
|
|
============
|
|
|
|
- Paper link: [https://arxiv.org/abs/1710.10903](https://arxiv.org/abs/1710.10903)
|
|
- Author's code repo (in Tensorflow):
|
|
[https://github.com/PetarV-/GAT](https://github.com/PetarV-/GAT).
|
|
- Popular pytorch implementation:
|
|
[https://github.com/Diego999/pyGAT](https://github.com/Diego999/pyGAT).
|
|
|
|
Dependencies
|
|
------------
|
|
- torch v1.0: the autograd support for sparse mm is only available in v1.0.
|
|
- requests
|
|
- sklearn
|
|
|
|
```bash
|
|
pip install torch==1.0.0 requests
|
|
```
|
|
|
|
How to run
|
|
----------
|
|
|
|
Run with following:
|
|
|
|
```bash
|
|
python3 train.py --dataset=cora --gpu=0
|
|
```
|
|
|
|
```bash
|
|
python3 train.py --dataset=citeseer --gpu=0 --early-stop
|
|
```
|
|
|
|
```bash
|
|
python3 train.py --dataset=pubmed --gpu=0 --num-out-heads=8 --weight-decay=0.001 --early-stop
|
|
```
|
|
|
|
```bash
|
|
python3 train_ppi.py --gpu=0
|
|
```
|
|
|
|
Results
|
|
-------
|
|
|
|
| Dataset | Test Accuracy | Time(s) | Baseline#1 times(s) | Baseline#2 times(s) |
|
|
| -------- | ------------- | ------- | ------------------- | ------------------- |
|
|
| Cora | 84.02(0.40) | 0.0113 | 0.0982 (**8.7x**) | 0.0424 (**3.8x**) |
|
|
| Citeseer | 70.91(0.79) | 0.0111 | n/a | n/a |
|
|
| Pubmed | 78.57(0.75) | 0.0115 | n/a | n/a |
|
|
| PPI | 0.9836 | n/a | n/a | n/a |
|
|
|
|
* All the accuracy numbers are obtained after 300 epochs.
|
|
* The time measures how long it takes to train one epoch.
|
|
* All time is measured on EC2 p3.2xlarge instance w/ V100 GPU.
|
|
* Baseline#1: [https://github.com/PetarV-/GAT](https://github.com/PetarV-/GAT).
|
|
* Baseline#2: [https://github.com/Diego999/pyGAT](https://github.com/Diego999/pyGAT).
|