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.
This example was implemented by Sinuo Xu when she was an undergraduate at SJTU.
Python 3.11.5 PyTorch 2.0.1 DGL 1.1.2 scikit-learn 1.3.1
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 |
python main.py --dataset cora
python main.py --dataset citeseer
python main.py --dataset pubmed| 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) |