Distribution-induced Bidirectional GAN for Graph Representation Learning

This is a TensorFlow implementation of the Distribution-induced Bidirectional GAN (DBGAN) model as described in our paper. Some of the code is borrowed from T. N. Kipf, M. Welling, Variational Graph Auto-Encoders[https://github.com/tkipf/gae] and Hu. R, ARGA [https://github.com/Ruiqi-Hu/ARGA]

Introduction

This code contains two versions of the hyper-parameters. The first one is the implementation of node clustering task. The second one is the implementation of link prediction task.

Requirements

• TensorFlow >= 1.12.0
• python 3.6
• networkx
• scikit-learn
• scipy
• dppy==0.2.0
• munkres

Run from

preset version:

python run.py

or modifying the network parameters and run

python run.py --hidden3 xxx --hidden2 xxx --learning_rate xxx ...

You can select the dataset in run.py

Data

If you want 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

Cite

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

@inproceedings{zheng2020distribution,
  title={Distribution-induced bidirectional generative adversarial network for graph representation learning},
  author={Zheng, Shuai and Zhu, Zhenfeng and Zhang, Xingxing and Liu, Zhizhe and Cheng, Jian and Zhao, Yao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7224--7233},
  year={2020}
}