https://github.com/TrustAGI-Lab/ARGA

This is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper: Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf].
https://github.com/TrustAGI-Lab/ARGA

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This is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper: Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf].

• Host: GitHub
• URL: https://github.com/TrustAGI-Lab/ARGA
• Owner: TrustAGI-Lab
• License: mit
• Created: 2018-07-25T10:35:17.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-02-28T08:59:28.000Z (almost 3 years ago)
• Last Synced: 2024-05-19T00:37:45.715Z (7 months ago)
• Language: Python
• Homepage:
• Size: 5.17 MB
• Stars: 185
• Watchers: 7
• Forks: 60
• Open Issues: 14
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• StarryDivineSky - Ruiqi-Hu/ARGA - >VGAE->ARGA (其他_图神经网络GNN / 网络服务_其他)
• graph-networks - Adversarially Regularized Graph Autoencoder

README

        
Adversarially Regularized Graph Autoencoder (ARGA)

============

This is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper:

 

Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf], published in IJCAI 2018: 2609-2615.

![Construction of ARGA](https://github.com/Ruiqi-Hu/ARGA/blob/master/ARGA_FLOW.jpg)

We borrowed part of code from T. N. Kipf, M. Welling, Variational Graph Auto-Encoders [https://github.com/tkipf/gae]

## Installation

```bash

pip install -r requirements.txt

```

## Requirements

* TensorFlow (1.0 or later)

* python 2.7

* networkx

* scikit-learn

* scipy

## Run from

```bash

python run.py

```

## Data

In order to use your own data, you have to provide 

* an N by N adjacency matrix (N is the number of nodes), and

* an N by D feature matrix (D is the number of features per node) -- optional

Have a look at the `load_data()` function in `input_data.py` for an example.

In this example, we load citation network data (Cora, Citeseer or Pubmed). The original datasets can be found here: http://linqs.cs.umd.edu/projects/projects/lbc/ and here (in a different format): https://github.com/kimiyoung/planetoid

## Models

You can choose between the following models: 

* `arga_ae`: Adversarially Regularised Graph Auto-Encoder

* `arga_vae`: Adversarially Regularised Variational Graph Auto-Encoder 

## Cite

Please cite following papers if you use this code in your own work:

```

@inproceedings{pan2018adversarially,

  title={Adversarially Regularized Graph Autoencoder for Graph Embedding.},

  author={Pan, Shirui and Hu, Ruiqi and Long, Guodong and Jiang, Jing and Yao, Lina and Zhang, Chengqi},

  booktitle={IJCAI},

  pages={2609--2615},

  year={2018}

}

```