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
55 行
1.4 KiB
Markdown
55 行
1.4 KiB
Markdown
Graph Attention Networks (GAT)
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============
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- Paper link: [https://arxiv.org/abs/1710.10903](https://arxiv.org/abs/1710.10903)
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- Author's code repo (in Tensorflow):
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[https://github.com/PetarV-/GAT](https://github.com/PetarV-/GAT).
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- Popular pytorch implementation:
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[https://github.com/Diego999/pyGAT](https://github.com/Diego999/pyGAT).
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Dependencies
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------------
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- torch v1.0: the autograd support for sparse mm is only available in v1.0.
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- requests
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- sklearn
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```bash
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pip install torch==1.0.0 requests
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```
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How to run
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----------
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Run with following:
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```bash
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python3 train.py --dataset=cora --gpu=0
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```
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```bash
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python3 train.py --dataset=citeseer --gpu=0
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```
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```bash
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python3 train.py --dataset=pubmed --gpu=0 --num-out-heads=8 --weight-decay=0.001
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```
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```bash
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python3 train_ppi.py --gpu=0
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```
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Results
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-------
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| Dataset | Test Accuracy | Time(s) | Baseline#1 times(s) | Baseline#2 times(s) |
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| ------- | ------------- | ------- | ------------------- | ------------------- |
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| Cora | 84.0% | 0.0113 | 0.0982 (**8.7x**) | 0.0424 (**3.8x**) |
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| Citeseer | 70.7% | 0.0111 | n/a | n/a |
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| Pubmed | 78.0% | 0.0115 | n/a | n/a |
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* All the accuracy numbers are obtained after 300 epochs.
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* The time measures how long it takes to train one epoch.
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* All time is measured on EC2 p3.2xlarge instance w/ V100 GPU.
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* Baseline#1: [https://github.com/PetarV-/GAT](https://github.com/PetarV-/GAT).
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* Baseline#2: [https://github.com/Diego999/pyGAT](https://github.com/Diego999/pyGAT).
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