wehub-resource-sync
55 lines
1.5 KiB
Markdown
55 lines
1.5 KiB
Markdown
# DGL Implementation of the GeniePath Paper
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This DGL example implements the GNN model proposed in the paper [GeniePath: Graph Neural Networks with Adaptive Receptive Paths](https://arxiv.org/abs/1802.00910).
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Example implementor
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----------------------
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This example was implemented by [Kay Liu](https://github.com/kayzliu) during his SDE intern work at the AWS Shanghai AI Lab.
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Dependencies
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----------------------
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- Python 3.7.10
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- PyTorch 1.8.1
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- dgl 0.7.0
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- scikit-learn 0.23.2
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Dataset
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The datasets used for node classification are [Pubmed citation network dataset](https://docs.dgl.ai/api/python/dgl.data.html#dgl.data.PubmedGraphDataset) (tranductive) and [Protein-Protein Interaction dataset](https://docs.dgl.ai/api/python/dgl.data.html#dgl.data.PPIDataset) (inductive).
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How to run
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If want to train on Pubmed (transductive), run
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```
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python pubmed.py
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```
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If want to use a GPU, run
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```
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python pubmed.py --gpu 0
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```
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If want to train GeniePath-Lazy, run
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```
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python pubmed.py --lazy True
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```
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If want to train on PPI (inductive), run
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```
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python ppi.py
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```
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Performance
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Dataset: Pubmed (ACC)
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|Method | GeniePath|
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| ------ | ----------- |
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| Paper | 78.5% |
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| DGL | 73.0% |
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Dataset: PPI (micro-F1)
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|Method | GeniePath| GeniePath-lazy| GeniePath-lazy-residual|
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| ------ | ----------- | ------------- | ------------------ |
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| Paper | 0.9520 | 0.9790 | 0.9850 |
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| DGL | 0.9729 | 0.9802 | 0.9798 |
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