GNN4LP 0.1.0
gnn for link prediction
License: Apache 2.0
Requires Python: >=3.6
Keywords: NLP,Link Prediction,gnn for link prediction

# gnn for link prediction（gnn4lp）

利用图神经网络进行链接预测（link prediction）。

**Guide**

- [Intro](#Intro)
- [Model](#Model)
- [Dataset](#Dataset)
- [Install](#install)
- [Cite](#Cite)
- [Reference](#reference)

## Intro

本项目是对此前项目[gcn_for_prediction_of_protein_interactions](https://github.com/jiangnanboy/gcn_for_prediction_of_protein_interactions) 的改动，使其应用于链接预测（link prediction），可以应用于两种数据集：a.带节点特征；b.不带节点特征。

a.带节点特征数据集如【data/cora】

b.不带节点特征数据集如【data/yeast】

## Model
### 模型
模型主要使用图神经网络，如gae、vgae等
* 1.GCNModelVAE(src/vgae)：图卷积自编码和变分图卷积自编码(config中可配置使用自编码或变分自编码)，利用gae/vgae作为编码器，InnerProductDecoder作解码器。 [Variational Graph Auto-Encoders](https://arxiv.org/pdf/1611.07308.pdf) 。

    ![image](https://raw.githubusercontent.com/jiangnanboy/gnn4lp/master/image/vgae.png)
* 2.GCNModelARGA(src/arga)：对抗正则化图自编码，利用gae/vgae作为生成器；一个三层前馈网络作判别器。 [Adversarially Regularized Graph Autoencoder for Graph Embedding](https://arxiv.org/pdf/1802.04407v2.pdf) 。

    ![image](https://raw.githubusercontent.com/jiangnanboy/gnn4lp/master/image/arga.png)
* 3.GATModelVAE(src/graph_att_gae)：基于图注意力的图卷积自编码和变分图卷积自编码(config中可配置使用自编码或变分自编码)，利用gae/vgae作为编码器，InnerProductDecoder作解码器。这是我在以上【1】方法的基础上加入了一层图注意力层，关于图注意力可见【Reference】中的【GRAPH ATTENTION NETWORKS】。
* 4.GATModelGAN(src/graph_att_gan)：基于图注意力的对抗正则化图自编码，利用gae/vgae作为生成器；一个三层前馈网络作判别器，这是我在以上【2】方法的基础上加入了一层图注意力层，关于图注意力可见【Reference】中的【GRAPH ATTENTION NETWORKS】。
* 5.NHGATModelVAE(src/graph_nheads_att_gae)：基于图多头注意力的图卷积自编码和变分图卷积自编码(config中可配置使用自编码或变分自编码)，利用gae/vgae作为编码器，InnerProductDecoder作解码器。此方法是在【3】方法的基础上将图注意力层改为多头注意力层。
* 6.NHGATModelGAN(src/graph_nheads_att_gan)：基于图多头注意力的对抗正则化图自编码，利用gae/vgae作为生成器；一个三层前馈网络作判别器，此方法在【4】方法的基础上将图注意力层改为多头注意力层。

#### Usage
- 相关参数的配置config见每个模型文件夹中的config.cfg文件，训练和预测时会加载此文件。

- 训练及预测

    ##### 1.model comparison
    (1).不带节点特征数据集【data/yeast】
    
    |   | GCNModelVAE  | GCNModelARGA  | GATModelVAE  | GATModelGAN  |NHGATModelVAE|  NHGATModelVAE  |
    | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ |
    | epochs     | 20  | 20  | 20  | 20  |20|20  |
    | precision  | 0.8708  | 0.8700  | 0.8629  | 0.8715  |0.8672|0.8680|
    | roc_score  | 0.8814 |  0.8798  | 0.8758  | 0.8826  |0.8770|0.8767|
    
    (2).带节点特征数据集【data/cora】
    
    |   | GCNModelVAE  | GCNModelARGA  | GATModelVAE  | GATModelGAN  |NHGATModelVAE|  NHGATModelVAE  |
    | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ |
    | epochs     | 200  | 200  | 200  | 200  |200|200  |
    | precision  | 0.7451  | 0.8544  | 0.8509  | 0.8392  |0.8078|0.8156|
    | roc_score  | 0.7404 |  0.8437  | 0.8686  | 0.8312  |0.8278|0.8240|
    
    #### 2.usage
    (1).train
    
    是否带节点特征可通过每个model文件夹中的【config.cfg】进行配置，如下：
    
    config中有以下三个参数
    ```
    node_cites_path = cora.cites # 节点间的引用数据
    node_features_path = cora.content ＃ 节点特征数据
    with_feats = True # 是否带节点特征。True, node_features_path不为None；False, node_features_path为None
    ```
    
    ```
    # train代码通用格式：
    from src.graph_nheads_att_gan.train import Train
    
    train = Train()
    train.train_model('config.cfg')
    ```
  
    (2).predict

    ```
    # predict代码通用格式:
    from src.graph_nheads_att_gan.predict import Predict
  
    predict = Predict()
    predict.load_model_adj('config_cfg')
    # 会返回原始的图邻接矩阵和经过模型编码后的hidden embedding经过内积解码的邻接矩阵，可以对这两个矩阵进行比对，得出link prediction.
    adj_orig, adj_rec = predict.predict()
    ```
  
## Dataset
   #####a. yeast dataset，【data/yeast】
   数据来自酵母蛋白质相互作用[yeast](http://snap.stanford.edu/deepnetbio-ismb/ipynb/yeast.edgelist) 。
   数据集的格式如下，具体可见[data](data/yeast/yeast.edgelist)。
   ```
    YLR418C	YOL145C
    YOL145C	YLR418C
    YLR418C	YOR123C
    YOR123C	YLR418C
    ......         ......
   ```
   #####b. cora dataset，【data/cora】
   数据来自Machine Learning Paper的数据集[cora](https://linqs.soe.ucsc.edu/data) 。
   数据集的格式如下，具体可见[data](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz) 。
   
   cora.cites:
   ```
    35	1033
    35	103482
    35	103515
    35	1050679
    ...... ......
   ```
   cora.content:
   ```
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    ...
   ```

## Install
* 安装：pip install GNN4LP
* 下载源码：
```
git clone https://github.com/jiangnanboy/gnn4lp.git
cd gnn4lp
python setup.py install
```


通过以上两种方法的任何一种完成安装都可以。如果不想安装，可以下载[github源码包](https://github.com/jiangnanboy/gnn4lp/archive/refs/heads/master.zip)

## Cite

如果你在研究中使用了GNN4LP，请按如下格式引用：

```latex
@software{GNN4LP,
  author = {Shi Yan},
  title = {GNN4LP: gnn for link prediction},
  year = {2021},
  url = {https://github.com/jiangnanboy/gnn4lp},
}
```    

## Reference

* [Variational Graph Auto-Encoders](https://arxiv.org/pdf/1611.07308.pdf)
* https://github.com/zfjsail/gae-pytorch/blob/master/gae/utils.py
* https://github.com/tkipf/gae/tree/master/gae
* http://snap.stanford.edu/deepnetbio-ismb/ipynb/Graph+Convolutional+Prediction+of+Protein+Interactions+in+Yeast.html
* [Adversarially Regularized Graph Autoencoder for Graph Embedding](https://arxiv.org/pdf/1802.04407v2.pdf)
* https://github.com/pyg-team/pytorch_geometric
* [GRAPH ATTENTION NETWORKS](https://arxiv.org/pdf/1710.10903.pdf)