wehub-resource-sync
52 lines
1.5 KiB
Markdown
52 lines
1.5 KiB
Markdown
# DGL Implementation of DeeperGCN
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This DGL example implements the GNN model proposed in the paper [DeeperGCN: All You Need to Train Deeper GCNs](https://arxiv.org/abs/2006.07739). For the original implementation, see [here](https://github.com/lightaime/deep_gcns_torch).
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Contributor: [xnuohz](https://github.com/xnuohz)
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### Requirements
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The codebase is implemented in Python 3.7. For version requirement of packages, see below.
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```
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dgl 0.6.0.post1
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torch 1.7.0
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ogb 1.3.0
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```
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### The graph datasets used in this example
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Open Graph Benchmark(OGB). Dataset summary:
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###### Graph Property Prediction
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| Dataset | #Graphs | #Node Feats | #Edge Feats | Metric |
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| :---------: | :-----: | :---------: | :---------: | :-----: |
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| ogbg-molhiv | 41,127 | 9 | 3 | ROC-AUC |
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### Usage
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Train a model which follows the original hyperparameters on different datasets.
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```bash
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# ogbg-molhiv
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python main.py --gpu 0 --learn-beta
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```
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### Performance
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* Table 6: Numbers associated with "Table 6" are the ones from table 6 in the paper.
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* Author: Numbers associated with "Author" are the ones we got by running the original code.
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* DGL: Numbers associated with "DGL" are the ones we got by running the DGL example.
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| Dataset | ogbg-molhiv |
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| :--------------: | :---------: |
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| Results(Table 6) | 0.786 |
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| Results(Author) | 0.781 |
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| Results(DGL) | 0.778 |
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### Speed
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| Dataset | ogbg-molhiv |
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| :-------------: | :---------: |
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| Results(Author) | 11.833 |
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| Results(DGL) | 8.965 |
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