PyTorch 1.6 and Python 3.7 implementation of Graph Convolutional Networks (GCNs) for semi-supervised classification [1].
Tested on the cora/pubmed/citeseer data set, the code on this repository can achieve the effect of the paper.
| dataset | Citeseea | Cora | Pubmed |
|---|---|---|---|
| GCN(official) | 70.3 | 81.5 | 79.0 |
| This repo. | 70.7 | 81.2 | 79.2 |
NOTE: The result of the experiment is to repeat the run 10 times, and then take the average of accuracy.
- PyTorch==1.6.0
- Python==3.7
- dgl==0.5.2
- scipy==1.5.2
- numpy==1.19.1
- networkx==2.5
python train.py --dataset cora[1] Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016