This DGL example implements the method proposed in the paper Learning from Labeled and Unlabeled Data with Label Propagation.
Contributor: xnuohz
The codebase is implemented in Python 3.7. For version requirement of packages, see below.
dgl 0.6.0.post1
torch 1.7.0
The DGL's built-in Cora, Pubmed and Citeseer datasets. Dataset summary:
| 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 |
# Cora
python main.py
# Citeseer
python main.py --dataset Citeseer --num-layers 100 --alpha 0.99
# Pubmed
python main.py --dataset Pubmed --num-layers 60 --alpha 1| Dataset | Cora | Citeseer | Pubmed |
|---|---|---|---|
| Results(DGL) | 69.20 | 51.30 | 71.40 |