wehub-resource-sync
87 lines
1.9 KiB
Markdown
87 lines
1.9 KiB
Markdown
# HardGAT
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## DGL Implementation of h/cGAO paper.
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This DGL example implements the GNN model proposed in the paper [HardGraphAttention](https://arxiv.org/abs/1907.04652.pdf).
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HardGANet implementor
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----------------------
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This example was implemented by [Ericcsr](https://github.com/Ericcsr) during his Internship work at the AWS Shanghai AI Lab.
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The graph dataset used in this example
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The DGL's built-in CoraGraphDataset. Dataset summary:
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- NumNodes: 2708
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- NumEdges: 10556
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- NumFeats: 1433
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- NumClasses: 7
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- NumTrainingSamples: 140
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- NumValidationSamples: 500
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- NumTestSamples: 1000
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The DGL's build-in CiteseerGraphDataset. Dataset Summary:
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- NumNodes: 3327
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- NumEdges: 9228
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- NumFeats: 3703
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- NumClasses: 6
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- NumTrainingSamples: 120
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- NumValidationSamples: 500
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- NumTestSamples: 1000
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The DGL's build-in PubmedGraphDataset. Dataset Summary:
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- NumNodes: 19717
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- NumEdges: 88651
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- NumFeats: 500
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- NumClasses: 3
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- NumTrainingSamples: 60
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- NumValidationSamples: 500
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- NumTestSamples: 1000
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How to run example files
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In the hgao folder, run
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**Please use `train.py`**
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```python
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python train.py --dataset=cora
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```
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If want to use a GPU, run
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```python
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python train.py --gpu 0 --dataset=citeseer
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```
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If you want to use more Graph Hard Attention Modules
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```python
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python train.py --num-layers <your number> --dataset=pubmed
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```
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If you want to change the hard attention threshold k
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```python
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python train.py --k <your number> --dataset=cora
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```
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If you want to test with vanillia GAT
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```python
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python train.py --model <gat/hgat> --dataset=cora
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```
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Performance
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-------------------------
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| Models/Datasets | Cora | Citeseer | Pubmed |
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| :-------------- | :--: | :------: | -----: |
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| GAT in DGL | 81.5% | 70.1% | 77.7% |
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| HardGAT | 81.8% | 70.2% |78.0%|
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Notice that HardGAT Simply replace GATConv with hGAO mentioned in paper.
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