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.

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

Installation

pip install -r requirements.txt

Requirements

• TensorFlow (1.0 or later)
• python 2.7
• networkx
• scikit-learn
• scipy

Run from

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}
}