vgae

Variational 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