https://github.com/lrjconan/GRAN

Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019
https://github.com/lrjconan/GRAN

deep-generative-model generative-model graph-generation graph-neural-networks neurips-2019

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Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019

• Host: GitHub
• URL: https://github.com/lrjconan/GRAN
• Owner: lrjconan
• License: mit
• Created: 2019-09-26T00:44:58.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-08-03T03:21:22.000Z (about 1 year ago)
• Last Synced: 2024-06-18T13:48:15.783Z (3 months ago)
• Topics: deep-generative-model, generative-model, graph-generation, graph-neural-networks, neurips-2019
• Language: C++
• Homepage:
• Size: 7.62 MB
• Stars: 457
• Watchers: 11
• Forks: 95
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

# GRAN

This is the official PyTorch implementation of [Efficient Graph Generation with Graph Recurrent Attention Networks](https://arxiv.org/abs/1910.00760) as described in the following NeurIPS 2019 paper:

```

@inproceedings{liao2019gran,

  title={Efficient Graph Generation with Graph Recurrent Attention Networks}, 

  author={Liao, Renjie and Li, Yujia and Song, Yang and Wang, Shenlong and Nash, Charlie and Hamilton, William L. and Duvenaud, David and Urtasun, Raquel and Zemel, Richard}, 

  booktitle={NeurIPS},

  year={2019}

}

```

## Visualization

### Generation of GRAN per step:

![](http://www.cs.toronto.edu/~rjliao/imgs/gran_model.gif)

### Overall generation process:



## Dependencies

Python 3, PyTorch(1.2.0)

Other dependencies can be installed via 

  ```pip install -r requirements.txt```

## Run Demos

### Train

* To run the training of experiment ```X``` where ```X``` is one of {```gran_grid```, ```gran_DD```, ```gran_DB```, ```gran_lobster```}:

  ```python run_exp.py -c config/X.yaml```

  

**Note**:

* Please check the folder ```config``` for a full list of configuration yaml files.

* Most hyperparameters in the configuration yaml file are self-explanatory.

### Test

* After training, you can specify the ```test_model``` field of the configuration yaml file with the path of your best model snapshot, e.g.,

  ```test_model: exp/gran_grid/xxx/model_snapshot_best.pth```	

* To run the test of experiments ```X```:

  ```python run_exp.py -c config/X.yaml -t```

**Note**:

* Please check the [evaluation](https://github.com/JiaxuanYou/graph-generation) to set up.

### Trained Models

* You could use our trained model for comparisons. Please make sure you are using the same split of the dataset. Running the following script will download the trained model:

	```./download_model.sh```	

## Sampled Graphs from GRAN

* Proteins Graphs from Training Set:

![](http://www.cs.toronto.edu/~rjliao/imgs/protein_train.png)

* Proteins Graphs Sampled from GRAN:

![](http://www.cs.toronto.edu/~rjliao/imgs/protein_sample.png)

## Cite

Please cite our paper if you use this code in your research work.

## Questions/Bugs

Please submit a Github issue or contact [email protected] if you have any questions or find any bugs.