gat

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 --early-stop

python3 train.py --dataset=pubmed --gpu=0 --num-out-heads=8 --weight-decay=0.001 --early-stop

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.
• Baseline#2: https://github.com/Diego999/pyGAT.