wehub-resource-sync
43 lines
1.3 KiB
Markdown
43 lines
1.3 KiB
Markdown
# DGL Implementation of Label Propagation
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This DGL example implements the method proposed in the paper [Learning from Labeled and Unlabeled Data with Label Propagation](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.14.3864&rep=rep1&type=pdf).
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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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```
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### The graph datasets used in this example
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The DGL's built-in Cora, Pubmed and Citeseer datasets. Dataset summary:
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| Dataset | #Nodes | #Edges | #Feats | #Classes | #Train Nodes | #Val Nodes | #Test Nodes |
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| :------: | :----: | :----: | :----: | :------: | :----------: | :--------: | :---------: |
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| Citeseer | 3,327 | 9,228 | 3,703 | 6 | 120 | 500 | 1000 |
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| Cora | 2,708 | 10,556 | 1,433 | 7 | 140 | 500 | 1000 |
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| Pubmed | 19,717 | 88,651 | 500 | 3 | 60 | 500 | 1000 |
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### Usage
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```bash
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# Cora
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python main.py
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# Citeseer
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python main.py --dataset Citeseer --num-layers 100 --alpha 0.99
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# Pubmed
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python main.py --dataset Pubmed --num-layers 60 --alpha 1
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```
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### Performance
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| Dataset | Cora | Citeseer | Pubmed |
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| :----------: | :---: | :------: | :----: |
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| Results(DGL) | 69.20 | 51.30 | 71.40 |
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