This repository is a PyTorch implementation of GraphGAN(arXiv).

GraphGAN: Graph Representation Learning With Generative Adversarial Nets
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo
32nd AAAI Conference on Artificial Intelligence, 2018

• data/: training and test data
• pre_train/: pre-trained node embeddings

  Note: the dimension of pre-trained node embeddings should equal n_emb in src/GraphGAN/config.py

• results/: evaluation results and the learned embeddings of the generator and the discriminator
• src/: source codes

The code has been tested running under Python 3.8.12, with the following packages installed (along with their dependencies):

• pytorch == 1.9.0
• cuda == 10.2
• tqdm == 4.62.3 (for displaying the progress bar)
• numpy == 1.21.2
• sklearn == 1.0.2

The input data should be an undirected graph in which node IDs start from 0 to N-1 (N is the number of nodes in the graph). Each line contains two node IDs indicating an edge in the graph.

0 1

3 2

...

Create directories to store your result and cache file.

mkdir cache
mkdir results

Before training, you should modify the PATH in config.py.

# src/GraphGAN/config.py
PATH = "/home/[YOUR NAME]/GraphGAN_pytorch/src/"

Then, train and evaluate GraphGAN for link prediction:

python graph_gan.py