Python实现基于图神经网络的异构图表示学习和推荐算法研究

我不是女神ヾ 2024-03-24 20:10 13阅读 0赞

[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/88243467  
[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/88243467

### 目录结构 ###

GNN-Recommendation/
        gnnrec/             算法模块顶级包
            hge/            异构图表示学习模块
            kgrec/          基于图神经网络的推荐算法模块
        data/               数据集目录（已添加.gitignore）
        model/              模型保存目录（已添加.gitignore）
        img/                图片目录
        academic_graph/     Django项目模块
        rank/               Django应用
        manage.py           Django管理脚本

### 安装依赖 ###

Python 3.7

#### CUDA 11.0 ####

pip install -r requirements_cuda.txt

#### CPU ####

pip install -r requirements.txt

### 异构图表示学习（附录） ###

基于对比学习的关系感知异构图神经网络(Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning, RHCO)

![b6a4ad8b48d6322d2f15de09e0cdc1bf.png][]

#### 实验 ####

见 readme

### 基于图神经网络的推荐算法(附录) ###

基于图神经网络的学术推荐算法(Graph Neural Network based Academic Recommendation Algorithm, GARec)

![23ecb6ba7a55376f3408ca41256df7fe.png][]

#### 实验 ####

见 readme

### Django 配置 ###

#### MySQL 数据库配置 ####

1.  创建数据库及用户

CREATE DATABASE academic_graph CHARACTER SET utf8mb4;
    CREATE USER 'academic_graph'@'%' IDENTIFIED BY 'password';
    GRANT ALL ON academic_graph.* TO 'academic_graph'@'%';

1.  在根目录下创建文件.mylogin.cnf

[client]
    host = x.x.x.x
    port = 3306
    user = username
    password = password
    database = database
    default-character-set = utf8mb4

1.  创建数据库表

python manage.py makemigrations --settings=academic_graph.settings.prod rank
    python manage.py migrate --settings=academic_graph.settings.prod

1.  导入 oag-cs 数据集

python manage.py loadoagcs --settings=academic_graph.settings.prod

注：由于导入一次时间很长（约 9 小时），为了避免中途发生错误，可以先用 data/oag/test 中的测试数据调试一下

#### 拷贝静态文件 ####

python manage.py collectstatic --settings=academic_graph.settings.prod

#### 启动 Web 服务器 ####

export SECRET_KEY=xxx
    python manage.py runserver --settings=academic_graph.settings.prod 0.0.0.0:8000

#### 系统截图 ####

搜索论文  
![9db5e93df68cb5308392bbd757503357.png][]

论文详情  
![44f93d593c150cd85bed650ed4b3f43d.png][]

搜索学者  
![967bd47c6acb5088680d1478699d780c.png][]

学者详情  
![4d4702abb9c28d543626d7834b326246.png][]

### 附录 ###

#### 基于图神经网络的推荐算法 ####

##### 数据集 #####

oag-cs - 使用 OAG 微软学术数据构造的计算机领域的学术网络（见 readme）

##### 预训练顶点嵌入 #####

使用 metapath2vec（随机游走 +word2vec）预训练顶点嵌入，作为 GNN 模型的顶点输入特征

1.  随机游走

python -m gnnrec.kgrec.random_walk model/word2vec/oag_cs_corpus.txt

1.  训练词向量

python -m gnnrec.hge.metapath2vec.train_word2vec --size=128 --workers=8 model/word2vec/oag_cs_corpus.txt model/word2vec/oag_cs.model

##### 召回 #####

使用微调后的 SciBERT 模型（见 readme 第 2 步）将查询词编码为向量，与预先计算好的论文标题向量计算余弦相似度，取 top k

python -m gnnrec.kgrec.recall

召回结果示例：

graph neural network

0.9629	Aggregation Graph Neural Networks
    0.9579	Neural Graph Learning: Training Neural Networks Using Graphs
    0.9556	Heterogeneous Graph Neural Network
    0.9552	Neural Graph Machines: Learning Neural Networks Using Graphs
    0.9490	On the choice of graph neural network architectures
    0.9474	Measuring and Improving the Use of Graph Information in Graph Neural Networks
    0.9362	Challenging the generalization capabilities of Graph Neural Networks for network modeling
    0.9295	Strategies for Pre-training Graph Neural Networks
    0.9142	Supervised Neural Network Models for Processing Graphs
    0.9112	Geometrically Principled Connections in Graph Neural Networks

recommendation algorithm based on knowledge graph

0.9172	Research on Video Recommendation Algorithm Based on Knowledge Reasoning of Knowledge Graph
    0.8972	An Improved Recommendation Algorithm in Knowledge Network
    0.8558	A personalized recommendation algorithm based on interest graph
    0.8431	An Improved Recommendation Algorithm Based on Graph Model
    0.8334	The Research of Recommendation Algorithm based on Complete Tripartite Graph Model
    0.8220	Recommendation Algorithm based on Link Prediction and Domain Knowledge in Retail Transactions
    0.8167	Recommendation Algorithm Based on Graph-Model Considering User Background Information
    0.8034	A Tripartite Graph Recommendation Algorithm Based on Item Information and User Preference
    0.7774	Improvement of TF-IDF Algorithm Based on Knowledge Graph
    0.7770	Graph Searching Algorithms for Semantic-Social Recommendation

scholar disambiguation

0.9690	Scholar search-oriented author disambiguation
    0.9040	Author name disambiguation in scientific collaboration and mobility cases
    0.8901	Exploring author name disambiguation on PubMed-scale
    0.8852	Author Name Disambiguation in Heterogeneous Academic Networks
    0.8797	KDD Cup 2013: author disambiguation
    0.8796	A survey of author name disambiguation techniques: 2010–2016
    0.8721	Who is Who: Name Disambiguation in Large-Scale Scientific Literature
    0.8660	Use of ResearchGate and Google CSE for author name disambiguation
    0.8643	Automatic Methods for Disambiguating Author Names in Bibliographic Data Repositories
    0.8641	A brief survey of automatic methods for author name disambiguation

#### 精排 ####

##### 构造 ground truth #####

（1）验证集

从 AMiner 发布的 [AI 2000 人工智能全球最具影响力学者榜单][AI 2000] 抓取人工智能 20 个子领域的 top 100 学者

pip install scrapy>=2.3.0
    cd gnnrec/kgrec/data/preprocess
    scrapy runspider ai2000_crawler.py -a save_path=/home/zzy/GNN-Recommendation/data/rank/ai2000.json

与 oag-cs 数据集的学者匹配，并人工确认一些排名较高但未匹配上的学者，作为学者排名 ground truth 验证集

export DJANGO_SETTINGS_MODULE=academic_graph.settings.common
    export SECRET_KEY=xxx
    python -m gnnrec.kgrec.data.preprocess.build_author_rank build-val

（2）训练集

参考 AI 2000 的计算公式，根据某个领域的论文引用数加权求和构造学者排名，作为 ground truth 训练集

计算公式：  
![91741105a433d89387bfddb73fc53989.png][]  
即：假设一篇论文有 n 个作者，第 k 作者的权重为 1/k，最后一个视为通讯作者，权重为 1/2，归一化之后计算论文引用数的加权求和

python -m gnnrec.kgrec.data.preprocess.build_author_rank build-train

（3）评估 ground truth 训练集的质量

python -m gnnrec.kgrec.data.preprocess.build_author_rank eval

nDGC@100=0.2420 Precision@100=0.1859    Recall@100=0.2016
    nDGC@50=0.2308  Precision@50=0.2494     Recall@50=0.1351
    nDGC@20=0.2492  Precision@20=0.3118     Recall@20=0.0678
    nDGC@10=0.2743  Precision@10=0.3471     Recall@10=0.0376
    nDGC@5=0.3165   Precision@5=0.3765      Recall@5=0.0203

（4）采样三元组

从学者排名训练集中采样三元组(t, ap, an)，表示对于领域 t，学者 ap 的排名在 an 之前

python -m gnnrec.kgrec.data.preprocess.build_author_rank sample

##### 训练 GNN 模型 #####

python -m gnnrec.kgrec.train model/word2vec/oag-cs.model model/garec_gnn.pt data/rank/author_embed.pt

### 异构图表示学习 ###

#### 数据集 ####

*  [ACM][] \- ACM 学术网络数据集
 *  [DBLP][] \- DBLP 学术网络数据集
 *  [ogbn-mag][] \- OGB 提供的微软学术数据集
 *  oag-venue \- oag-cs 期刊分类数据集

<table> 
 <thead> 
  <tr> 
   <th>数据集</th> 
   <th>顶点数</th> 
   <th>边数</th> 
   <th>目标顶点</th> 
   <th>类别数</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>ACM</td> 
   <td>11246</td> 
   <td>34852</td> 
   <td>paper</td> 
   <td>3</td> 
  </tr> 
  <tr> 
   <td>DBLP</td> 
   <td>26128</td> 
   <td>239566</td> 
   <td>author</td> 
   <td>4</td> 
  </tr> 
  <tr> 
   <td>ogbn-mag</td> 
   <td>1939743</td> 
   <td>21111007</td> 
   <td>paper</td> 
   <td>349</td> 
  </tr> 
  <tr> 
   <td>oag-venue</td> 
   <td>4235169</td> 
   <td>34520417</td> 
   <td>paper</td> 
   <td>360</td> 
  </tr> 
 </tbody> 
</table>

#### Baselines ####

*  [R-GCN][]
 *  [HGT][]
 *  [HGConv][]
 *  [R-HGNN][]
 *  [C&S][C_S]
 *  [HeCo][]

##### R-GCN (full batch) #####

python -m gnnrec.hge.rgcn.train --dataset=acm --epochs=10
    python -m gnnrec.hge.rgcn.train --dataset=dblp --epochs=10
    python -m gnnrec.hge.rgcn.train --dataset=ogbn-mag --num-hidden=48
    python -m gnnrec.hge.rgcn.train --dataset=oag-venue --num-hidden=48 --epochs=30

（使用 minibatch 训练准确率就是只有 20% 多，不知道为什么）

##### 预训练顶点嵌入 #####

使用 metapath2vec（随机游走 +word2vec）预训练顶点嵌入，作为 GNN 模型的顶点输入特征

python -m gnnrec.hge.metapath2vec.random_walk model/word2vec/ogbn-mag_corpus.txt
    python -m gnnrec.hge.metapath2vec.train_word2vec --size=128 --workers=8 model/word2vec/ogbn-mag_corpus.txt model/word2vec/ogbn-mag.model

##### HGT #####

python -m gnnrec.hge.hgt.train_full --dataset=acm
    python -m gnnrec.hge.hgt.train_full --dataset=dblp
    python -m gnnrec.hge.hgt.train --dataset=ogbn-mag --node-embed-path=model/word2vec/ogbn-mag.model --epochs=40
    python -m gnnrec.hge.hgt.train --dataset=oag-venue --node-embed-path=model/word2vec/oag-cs.model --epochs=40

##### HGConv #####

python -m gnnrec.hge.hgconv.train_full --dataset=acm --epochs=5
    python -m gnnrec.hge.hgconv.train_full --dataset=dblp --epochs=20
    python -m gnnrec.hge.hgconv.train --dataset=ogbn-mag --node-embed-path=model/word2vec/ogbn-mag.model
    python -m gnnrec.hge.hgconv.train --dataset=oag-venue --node-embed-path=model/word2vec/oag-cs.model

##### R-HGNN #####

python -m gnnrec.hge.rhgnn.train_full --dataset=acm --num-layers=1 --epochs=15
    python -m gnnrec.hge.rhgnn.train_full --dataset=dblp --epochs=20
    python -m gnnrec.hge.rhgnn.train --dataset=ogbn-mag model/word2vec/ogbn-mag.model
    python -m gnnrec.hge.rhgnn.train --dataset=oag-venue --epochs=50 model/word2vec/oag-cs.model

##### C&S #####

python -m gnnrec.hge.cs.train --dataset=acm --epochs=5
    python -m gnnrec.hge.cs.train --dataset=dblp --epochs=5
    python -m gnnrec.hge.cs.train --dataset=ogbn-mag --prop-graph=data/graph/pos_graph_ogbn-mag_t5.bin
    python -m gnnrec.hge.cs.train --dataset=oag-venue --prop-graph=data/graph/pos_graph_oag-venue_t5.bin

##### HeCo #####

python -m gnnrec.hge.heco.train --dataset=ogbn-mag model/word2vec/ogbn-mag.model data/graph/pos_graph_ogbn-mag_t5.bin
    python -m gnnrec.hge.heco.train --dataset=oag-venue model/word2vec/oag-cs.model data/graph/pos_graph_oag-venue_t5.bin

（ACM 和 DBLP 的数据来自 [https://github.com/ZZy979/pytorch-tutorial/tree/master/gnn/heco][https_github.com_ZZy979_pytorch-tutorial_tree_master_gnn_heco] ，准确率和 Micro-F1 相等）

##### RHCO #####

基于对比学习的关系感知异构图神经网络(Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning, RHCO)

在 HeCo 的基础上改进：

*  网络结构编码器中的注意力向量改为关系的表示（类似于 R-HGNN）
 *  正样本选择方式由元路径条数改为预训练的 HGT 计算的注意力权重、训练集使用真实标签
 *  元路径视图编码器改为正样本图编码器，适配 mini-batch 训练
 *  Loss 增加分类损失，训练方式由无监督改为半监督
 *  在最后增加 C&S 后处理步骤

ACM

python -m gnnrec.hge.hgt.train_full --dataset=acm --save-path=model/hgt/hgt_acm.pt
    python -m gnnrec.hge.rhco.build_pos_graph_full --dataset=acm --num-samples=5 --use-label model/hgt/hgt_acm.pt data/graph/pos_graph_acm_t5l.bin
    python -m gnnrec.hge.rhco.train_full --dataset=acm data/graph/pos_graph_acm_t5l.bin

DBLP

python -m gnnrec.hge.hgt.train_full --dataset=dblp --save-path=model/hgt/hgt_dblp.pt
    python -m gnnrec.hge.rhco.build_pos_graph_full --dataset=dblp --num-samples=5 --use-label model/hgt/hgt_dblp.pt data/graph/pos_graph_dblp_t5l.bin
    python -m gnnrec.hge.rhco.train_full --dataset=dblp --use-data-pos data/graph/pos_graph_dblp_t5l.bin

ogbn-mag（第 3 步如果中断可使用–load-path 参数继续训练）

python -m gnnrec.hge.hgt.train --dataset=ogbn-mag --node-embed-path=model/word2vec/ogbn-mag.model --epochs=40 --save-path=model/hgt/hgt_ogbn-mag.pt
    python -m gnnrec.hge.rhco.build_pos_graph --dataset=ogbn-mag --num-samples=5 --use-label model/word2vec/ogbn-mag.model model/hgt/hgt_ogbn-mag.pt data/graph/pos_graph_ogbn-mag_t5l.bin
    python -m gnnrec.hge.rhco.train --dataset=ogbn-mag --num-hidden=64 --contrast-weight=0.9 model/word2vec/ogbn-mag.model data/graph/pos_graph_ogbn-mag_t5l.bin model/rhco_ogbn-mag_d64_a0.9_t5l.pt
    python -m gnnrec.hge.rhco.smooth --dataset=ogbn-mag model/word2vec/ogbn-mag.model data/graph/pos_graph_ogbn-mag_t5l.bin model/rhco_ogbn-mag_d64_a0.9_t5l.pt

oag-venue

python -m gnnrec.hge.hgt.train --dataset=oag-venue --node-embed-path=model/word2vec/oag-cs.model --epochs=40 --save-path=model/hgt/hgt_oag-venue.pt
    python -m gnnrec.hge.rhco.build_pos_graph --dataset=oag-venue --num-samples=5 --use-label model/word2vec/oag-cs.model model/hgt/hgt_oag-venue.pt data/graph/pos_graph_oag-venue_t5l.bin
    python -m gnnrec.hge.rhco.train --dataset=oag-venue --num-hidden=64 --contrast-weight=0.9 model/word2vec/oag-cs.model data/graph/pos_graph_oag-venue_t5l.bin model/rhco_oag-venue.pt
    python -m gnnrec.hge.rhco.smooth --dataset=oag-venue model/word2vec/oag-cs.model data/graph/pos_graph_oag-venue_t5l.bin model/rhco_oag-venue.pt

消融实验

python -m gnnrec.hge.rhco.train --dataset=ogbn-mag --model=RHCO_sc model/word2vec/ogbn-mag.model data/graph/pos_graph_ogbn-mag_t5l.bin model/rhco_sc_ogbn-mag.pt
    python -m gnnrec.hge.rhco.train --dataset=ogbn-mag --model=RHCO_pg model/word2vec/ogbn-mag.model data/graph/pos_graph_ogbn-mag_t5l.bin model/rhco_pg_ogbn-mag.pt

#### 实验结果 ####

顶点分类

参数敏感性分析

消融实验

[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/88243467  
[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/88243467

[Link 1]: https://download.csdn.net/download/sheziqiong/88243467
[b6a4ad8b48d6322d2f15de09e0cdc1bf.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/af19892e85e24bd7a1eae1d308896a66.png
[23ecb6ba7a55376f3408ca41256df7fe.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/370cd36ded7c4799969465681744fd61.png
[9db5e93df68cb5308392bbd757503357.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/519a903287774dca973ed6840e01d5c7.png
[44f93d593c150cd85bed650ed4b3f43d.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/795bb0b436924f8c991bb744886e09b0.png
[967bd47c6acb5088680d1478699d780c.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/1758bedcb82b494a9543c339affd9206.png
[4d4702abb9c28d543626d7834b326246.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/a66f46d917b44177a17d9bfdd0d5455a.png
[AI 2000]: https://www.aminer.cn/ai2000
[91741105a433d89387bfddb73fc53989.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/13/73d7890107d04a70881127985cf3a951.png
[ACM]: https://github.com/liun-online/HeCo/tree/main/data/acm
[DBLP]: https://github.com/liun-online/HeCo/tree/main/data/dblp
[ogbn-mag]: https://ogb.stanford.edu/docs/nodeprop/#ogbn-mag
[R-GCN]: https://arxiv.org/pdf/1703.06103
[HGT]: https://arxiv.org/pdf/2003.01332
[HGConv]: https://arxiv.org/pdf/2012.14722
[R-HGNN]: https://arxiv.org/pdf/2105.11122
[C_S]: https://arxiv.org/pdf/2010.13993
[HeCo]: https://arxiv.org/pdf/2105.09111
[https_github.com_ZZy979_pytorch-tutorial_tree_master_gnn_heco]: https://github.com/ZZy979/pytorch-tutorial/tree/master/gnn/heco