[Model] Improve GAT models

examples/mxnet/gat/gat_batch.py

@@ -1,4 +1,3 @@
-
 """
 Graph Attention Networks
 Paper: https://arxiv.org/abs/1710.10903

examples/pytorch/gat/README.md

@@ -2,15 +2,39 @@ Graph Attention Networks (GAT)
 ============
 
 - Paper link: [https://arxiv.org/abs/1710.10903](https://arxiv.org/abs/1710.10903)
-- Author's code repo:
+- 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).
 
-Note that the original code is implemented with Tensorflow for the paper.
+Requirements
+------------
+- torch v1.0: the autograd support for sparse mm is only available in v1.0.
+- requests
 
-Results
--------
+```bash
+pip install torch==1.0.0 requests
+```
+
+How to run
+----------
 
 Run with following (available dataset: "cora", "citeseer", "pubmed")
 ```bash
-python gat.py --dataset cora --gpu 0 --num-heads 8
+python train.py --dataset=cora --gpu=0
 ```
+
+Results
+-------
+
+| Dataset | Test Accuracy | Time(s) | Baseline#1 times(s) | Baseline#2 times(s) |
+| ------- | ------------- | ------- | ------------------- | ------------------- |
+| Cora | 84.0% | 0.0127 | 0.0982 (**7.7x**) | 0.0424 (**3.3x**) |
+| Citeseer | 70.5% | 0.0123 | n/a | n/a |
+| Pubmed | 77.3% | 0.0302 | n/a | n/a |
+
+* All the accuracy numbers are obtained after 200 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).