wehub-resource-syncVariational Graph Auto-Encoders
- Paper link：https://arxiv.org/abs/1611.07308
- Author's code repo：https://github.com/tkipf/gae
Requirements
- Pytorch
- Python 3.x
- DGL 0.6
- scikit-learn
Run the demo
Run with following (available dataset: "cora", "citeseer", "pubmed")
python train.py
Dataset
In this example, I use two kinds of data source. One from DGL's bulit-in dataset (CoraGraphDataset, CiteseerGraphDataset and PubmedGraphDataset), another from website https://github.com/kimiyoung/planetoid.
You can specify a dataset as follows:
python train.py --datasrc dgl --dataset cora // from DGL
python train.py --datasrc website --dataset cora // from website
Note: If you want to train by dataset from website, you should download folder https://github.com/kimiyoung/planetoid/tree/master/data. Then put it under project folder.
Results
Use area under the ROC curve (AUC) and average precision (AP) scores for each model on the test set. Numbers show mean results and standard error for 10 runs with random initializations on fixed dataset splits.
Dataset from DGL
| Dataset | AUC | AP |
|---|---|---|
| Cora | 91.8$\pm$ 0.01 | 92.5$\pm$0.01 |
| Citeseer | 89.2$\pm$0.02 | 90.8$\pm$0.01 |
| Pubmed | 94.5$\pm$0.01 | 94.6$\pm$0.01 |
Dataset from website
| Dataset | AUC | AP |
|---|---|---|
| Cora | 90.9$\pm$ 0.01 | 92.1$\pm$0.01 |
| Citeseer | 90.3$\pm$0.01 | 91.8$\pm$0.01 |
| Pubmed | 94.4$\pm$ 0.01 | 94.6$\pm$ 0.01 |
Reported results in paper
| Dataset | AUC | AP |
|---|---|---|
| Cora | 91.4$\pm$ 0.01 | 92.6$\pm$0.01 |
| Citeseer | 90.8$\pm$0.02 | 92.0$\pm$0.02 |
| Pubmed | 94.4$\pm$0.02 | 94.7$\pm$0.02 |