Simplifying Graph Convolutional Networks (SGC) is a powerful neural network designed for machine learning on graphs. Based on PGL, we reproduce SGC algorithms and reach the same level of indicators as the paper in citation network benchmarks.
The datasets contain three citation networks: CORA, PUBMED, CITESEER. The details for these three datasets can be found in the paper.
- paddlepaddle 1.5
- pgl
We train our models for 200 epochs and report the accuracy on the test dataset.
| Dataset | Accuracy | Speed with paddle 1.5 (epoch time) |
|---|---|---|
| Cora | 0.818 (paper: 0.810) | 0.0015s |
| Pubmed | 0.788 (paper: 0.789) | 0.0015s |
| Citeseer | 0.719 (paper: 0.719) | 0.0015s |
For examples, use gpu to train SGC on cora dataset.
python sgc.py --dataset cora --use_cuda
- dataset: The citation dataset "cora", "citeseer", "pubmed".
- use_cuda: Use gpu if assign use_cuda.