dmlc--dgl
Lingfan Yu
74e13eea61
* fix gat code to use latest edge softmax module * avoid transpose * update README * use edge_softmax op * mxnet edge softmax op * mxnet gat * update README * fix unittest * fix ci * fix mxnet nn test; relax criteria for prod reducer
1.4 KiB
1.4 KiB
Graph Attention Networks (GAT)
- Paper link: https://arxiv.org/abs/1710.10903
- Author's code repo (in Tensorflow): https://github.com/PetarV-/GAT.
- Popular pytorch implementation: https://github.com/Diego999/pyGAT.
Dependencies
- torch v1.0: the autograd support for sparse mm is only available in v1.0.
- requests
- sklearn
pip install torch==1.0.0 requests
How to run
Run with following:
python3 train.py --dataset=cora --gpu=0
python3 train.py --dataset=citeseer --gpu=0
python3 train.py --dataset=pubmed --gpu=0 --num-out-heads=8 --weight-decay=0.001
python3 train_ppi.py --gpu=0
Results
| Dataset | Test Accuracy | Time(s) | Baseline#1 times(s) | Baseline#2 times(s) |
|---|---|---|---|---|
| Cora | 84.0% | 0.0113 | 0.0982 (8.7x) | 0.0424 (3.8x) |
| Citeseer | 70.7% | 0.0111 | n/a | n/a |
| Pubmed | 78.0% | 0.0115 | 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.
- Baseline#2: https://github.com/Diego999/pyGAT.