wehub-resource-sync
45 lines
1.3 KiB
Markdown
45 lines
1.3 KiB
Markdown
# Optimized Graph Convolution (OGC)
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This DGL example implements the OGC method from the paper: [From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited](https://arxiv.org/abs/2309.13599).
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With only one trainable layer, OGC is a very simple but powerful graph convolution method.
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## Example Implementor
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This example was implemented by [Sinuo Xu](https://github.com/SinuoXu) when she was an undergraduate at SJTU.
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## Dependencies
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Python 3.11.5
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PyTorch 2.0.1
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DGL 1.1.2
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scikit-learn 1.3.1
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## Dataset
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The DGL's built-in Cora, Pubmed and Citeseer datasets, as follows:
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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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python main.py --dataset cora
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python main.py --dataset citeseer
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python main.py --dataset pubmed
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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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| OGC (DGL) | **86.9(±0.2)** | **77.4(±0.1)** | **83.6(±0.1)** |
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| OGC (Reported) | **86.9(±0.0)** | **77.4(±0.0)** | 83.4(±0.0) |
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