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Optimized Graph Convolution (OGC)
This DGL example implements the OGC method from the paper: From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited. With only one trainable layer, OGC is a very simple but powerful graph convolution method.
Example Implementor
This example was implemented by Sinuo Xu when she was an undergraduate at SJTU.
Dependencies
Python 3.11.5 PyTorch 2.0.1 DGL 1.1.2 scikit-learn 1.3.1
Dataset
The DGL's built-in Cora, Pubmed and Citeseer datasets, as follows:
| Dataset | #Nodes | #Edges | #Feats | #Classes | #Train Nodes | #Val Nodes | #Test Nodes |
|---|---|---|---|---|---|---|---|
| Citeseer | 3,327 | 9,228 | 3,703 | 6 | 120 | 500 | 1000 |
| Cora | 2,708 | 10,556 | 1,433 | 7 | 140 | 500 | 1000 |
| Pubmed | 19,717 | 88,651 | 500 | 3 | 60 | 500 | 1000 |
Usage
python main.py --dataset cora
python main.py --dataset citeseer
python main.py --dataset pubmed
Performance
| Dataset | Cora | Citeseer | Pubmed |
|---|---|---|---|
| OGC (DGL) | 86.9(±0.2) | 77.4(±0.1) | 83.6(±0.1) |
| OGC (Reported) | 86.9(±0.0) | 77.4(±0.0) | 83.4(±0.0) |