Python实现的图神经网络进行链接预测（link prediction）_链接预测 图神经网络

Guide

• Intro
• Model
• Dataset
• Install
• Cite
• Reference

Intro

本项目是对此前项目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 。

  

• 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 。

• 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。

• Note:程序【src/util/cora_class_one_hot_process.py】将cora数据集中cora.content中的节点label进行one-hot编码（处理后的数据集为：data/cora/cora.content_class），将其与features一起作为训练特征，这就相当于实现了条件变分自编码【conditional variational auroencoder (CVAE)】，从而在encoder与decoder中加入了节点label的限制。

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
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  # train代码通用格式：
  from src.graph_nheads_att_gan.train import Train
  
  train = Train()
  train.train_model('config.cfg')
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  (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()
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Dataset

a. yeast dataset，【data/yeast】

数据来自酵母蛋白质相互作用yeast 。
数据集的格式如下，具体可见data。

 YLR418C	YOL145C
 YOL145C	YLR418C
 YLR418C	YOR123C
 YOR123C	YLR418C
 ......         ......
1
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3
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5

b. cora dataset，【data/cora】

数据来自Machine Learning Paper的数据集cora 。
数据集的格式如下，具体可见data 。

cora.cites:

 35	1033
 35	103482
 35	103515
 35	1050679
 ...... ......
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cora.content:

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