wehub-resource-sync
62 lines
1.8 KiB
Markdown
62 lines
1.8 KiB
Markdown
# Variational Graph Auto-Encoders
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- Paper link：https://arxiv.org/abs/1611.07308
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- Author's code repo：https://github.com/tkipf/gae
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## Requirements
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- Pytorch
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- Python 3.x
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- DGL 0.6
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- scikit-learn
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## Run the demo
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Run with following (available dataset: "cora", "citeseer", "pubmed")
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```
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python train.py
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```
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## Dataset
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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.
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You can specify a dataset as follows:
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```
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python train.py --datasrc dgl --dataset cora // from DGL
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python train.py --datasrc website --dataset cora // from website
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```
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**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.
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## Results
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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.
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### Dataset from DGL
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| Dataset | AUC | AP |
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| -------- | -------------- | ------------- |
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| Cora | 91.8$\pm$ 0.01 | 92.5$\pm$0.01 |
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| Citeseer | 89.2$\pm$0.02 | 90.8$\pm$0.01 |
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| Pubmed | 94.5$\pm$0.01 | 94.6$\pm$0.01 |
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### Dataset from website
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| Dataset | AUC | AP |
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| -------- | -------------- | -------------- |
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| Cora | 90.9$\pm$ 0.01 | 92.1$\pm$0.01 |
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| Citeseer | 90.3$\pm$0.01 | 91.8$\pm$0.01 |
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| Pubmed | 94.4$\pm$ 0.01 | 94.6$\pm$ 0.01 |
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### Reported results in paper
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| Dataset | AUC | AP |
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| -------- | -------------- | ------------- |
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| Cora | 91.4$\pm$ 0.01 | 92.6$\pm$0.01 |
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| Citeseer | 90.8$\pm$0.02 | 92.0$\pm$0.02 |
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| Pubmed | 94.4$\pm$0.02 | 94.7$\pm$0.02 |
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