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GNN4LP-0.1.0
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
gnn for link prediction
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | GNN4LP-0.1.0 |
| نام | GNN4LP |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | ShiYan |
| ایمیل نویسنده | 2229029156@qq.com |
| آدرس صفحه اصلی | https://github.com/jiangnanboy/gnn4lp |
| آدرس اینترنتی | https://pypi.org/project/GNN4LP/ |
| مجوز | Apache 2.0 |
# 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) 。
* 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) 。
* 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)
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl GNN4LP-0.1.0:
pip install GNN4LP-0.1.0.whl
نصب پکیج tar.gz GNN4LP-0.1.0:
pip install GNN4LP-0.1.0.tar.gz