# hypergraphgan

**Repository Path**: tongtao0305/hypergraphgan

## Basic Information

- **Project Name**: hypergraphgan
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-08-27
- **Last Updated**: 2021-09-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## GraphGAN

- This repository is the implementation of [GraphGAN](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16611) ([arXiv](https://arxiv.org/abs/1711.08267)):
> 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

![](https://github.com/hwwang55/GraphGAN/blob/master/framework.jpg)

GraphGAN unifies two schools of graph representation learning methodologies: generative methods and discriminative methods, via adversarial training in a minimax game.
The generator is guided by the signals from the discriminator and improves its generating performance, while the discriminator is pushed by the generator to better distinguish ground truth from generated samples.
	


### Files in the folder
- `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


### Requirements
The code has been tested running under Python 3.6.5, with the following packages installed (along with their dependencies):

- torch == 1.3.1
- tqdm == 4.23.4 (for displaying the progress bar)
- numpy == 1.14.3
- sklearn == 0.19.1


### Input format
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.

##### txt file sample

```0	1```  
```3	2```  
```...```


### Basic usage
```mkdir cache```   
```cd src/GraphGAN```  
```python graph_gan.py```

squeue | grep smart_in

### data
cora、facebook、citeseer、pubmed和webkb
接下来你们要做的是把复现的代码在着几个数据集上先跑一遍
也是按随机删除20%到80%边来实验

srun -n 2 --gres gpu:1 -p sugon --pty bash
srun -n 2 --gres gpu:1 -p dell --pty bash
python graph_gan_torch.py --data cora --manifold Hyperboloid --curvature 1.0
python graph_gan_torch.py --data facebook --manifold Hyperboloid --curvature 1.0

- cora
  - 2708 个样本点，每个点代表一篇论文
  - 7 个类别，分别是：基于案例；遗传算法；神经网络；概率方法；强化学习；规则学习；理论