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0.9.1

Get Started

  • Installation
  • Structures in DHG
    • Introduction
    • Applications
    • Two Core Operations
      • The Spectral-Based Operations
      • The Spatial-Based Operations
    • What Can be Done with the Two Operations?
      • Add Early Self-loop and Late Self-Loop
        • Message Passing with Early Self-Loop
        • Message Passing with Late Self-Loop
      • Fuse Features Learned from the Spectral and Spatial Domain
      • Fuse Features Learned from different Structures
  • Learning on Low-Order Structures
    • Learning on Graph
      • Definition
      • Construction
      • Spectral-Based Learning
        • Smoothing with GCN’s Laplacian
        • Smoothing with Symmetrically Normalized Laplacian
        • Smoothing with Left (random-walk) Normalized Laplacian
      • Spatial-Based Learning
        • Message Propagation from Vertex to Vertex
        • Message Propagation from Vertex to Vertex with different Edge Weights
    • Learning on Directed Graph
      • Definition
      • Construction
      • Spectral-Based Learning
      • Spatial-Based Learning
        • Message Propagation from Source Vertex to Target Vertex
        • Message Propagation from Source Vertex to Target Vertex with different Edge Weights
        • Message Propagation from Target Vertex to Source Vertex
        • Message Propagation from Target Vertex to Source Vertex with different Edge Weights
    • Learning on Bipartite Graph
      • Definition
      • Construction
      • Spectral-Based Learning
        • Smoothing with GCN’s Laplacian
      • Spatial-Based Learning
        • Message Propagation from Vertices in Set \(U\) to Vertices in Set \(V\)
        • Message Propagation from Vertices in Set \(U\) to Vertices in Set \(V\) with different Edge Weights
        • Message Propagation from Vertices in Set \(V\) to Vertices in Set \(U\)
        • Message Propagation from Vertices in Set \(V\) to Vertices in Set \(U\) with different Edge Weights
  • Learning on High-Oder Structures
    • Learning on Hypergraph
      • Definition
      • Construction
      • Spectral-Based Learning
        • Smoothing with HGNN’s Laplacian
      • Spatial-Based Learning
        • Message Propagation from Vertex to Hyperedge
        • Message Propagation from Vertex to Hyperedge with different Edge Weights
        • Message Propagation from Hyperedge to Vertex
        • Message Propagation from Hyperedge to Vertex with different Edge Weights
        • Message Propagation from Vertex Set to Vertex Set
        • Message Propagation from Vertex Set to Vertex Set with different Edge Weights in Two Stages
  • Contribute to DHG
    • Coding Style
    • Testing
    • Building Documentation

Examples

  • Vertex Classification
    • On Graph
      • Models
      • Dataset
      • Results
      • GCN on Cora
        • Import Libraries
        • Define Functions
        • Main
        • Outputs
      • GAT on Cora
        • Import Libraries
        • Define Functions
        • Main
        • Outputs
      • HGNN on Cora
        • Import Libraries
        • Define Functions
        • Main
        • Outputs
      • HGNN+ on Cora
        • Import Libraries
        • Define Functions
        • Main
        • Outputs
    • On Hypergraph
      • Models
      • Dataset
      • Results
      • GCN on Cooking200
        • Import Libraries
        • Define Functions
        • Main
        • Outputs
      • HGNN on Cooking200
        • Import Libraries
        • Define Functions
        • Main
        • Outputs
      • HGNN+ on Cooking200
        • Import Libraries
        • Define Functions
        • Main
        • Outputs
  • User-Item Recommender
    • Models
    • Dataset
    • Results
    • NGCF on Gowalla
      • Import Libraries
      • Define Functions
      • Main
      • Outputs
    • LightGCN on Gowalla
      • Import Libraries
      • Define Functions
      • Main
      • Outputs
  • Auto Hyper-parameters Tuning
    • GCN on Cora
      • Configuration
      • Import Libraries
      • Define Functions
      • Main
      • Outputs
    • HGNN+ on Cooking200
      • Configuration
      • Import Libraries
      • Define Functions
      • Main
      • Outputs
    • LightGCN on Gowalla
      • Configuration
      • Import Libraries
      • Define Functions
      • Main
      • Outputs

Tutorials

  • Overview
    • Preparations
    • Training and Evaluation
    • Others
  • Building Structure
    • Low-Order Structures
      • Building Graph
        • Common Methods
        • Reduced from High-Order Structures
      • Building Directed Graph
        • Common Methods
        • Reduced from High-Order Structures
      • Building Bipartite Graph
        • Common Methods
        • Reduced from High-Order Structures
    • High-Order Structures
      • Building Hypergraph
        • Common Methods
        • Prometed from Low-Order Structures
  • Building Dataset
    • Usage
    • Architechture
    • Building Your Own Dataset
      • Example of Graph Dataset:
      • Example of Hypergraph Dataset
      • Example of User-Item Bipartite Dataset
  • Building Model
    • Building Spectral-based Model
    • Building Spatial-based Model
    • Building Hybrid Operation Model
    • Building Hybrid Structure Model
  • Building Evaluator
    • Initialization
    • Epoch Evaluation
    • Add Batches Then Do Epoch Evaluation
  • Training Model
    • Training without Batch Data
    • Training with Batch Data
  • Training with Auto ML
    • Builder Functions for Auto-ML
      • Defining the Structure Builder
      • Defining the Model Builder
      • Defining the Train Builder
    • Task Class for Auto-ML
    • Auto-ML for Vertex Classification Task
      • On Graph
      • On Hypergraph
    • Auto-ML for Item Recommender Task
  • Structure Generation
    • Random Graph Generation
    • Random Directed Graph Generation
    • Random Bipartite Graph Generation
    • Random Hypergraph Generation

中文文档

  • DHG简介
  • 上手指南
    • 安装
    • DHG内的关联结构
      • 简介
      • 应用场景
      • 两个核心操作
        • 基于谱域的操作
        • 基于空域的操作
      • 基于两种操作可以实现什么?
        • 增加先自环以及后自环
        • 融合从谱域和空域中学习到的特征
        • 融合从不同关联结构中学习到的特征
    • 低阶关联结构上的表示学习
      • 图上的表示学习
        • 定义
        • 结构构建
        • 基于谱域的学习
        • 基于空域的学习
      • 有向图上的表示学习
        • 定义
        • 结构构建
        • 基于谱域的学习
        • 基于空域的学习
      • 二分图上的表示学习
        • 定义
        • 结构构建
        • 基于谱域的学习
        • 基于空域的学习
    • 高阶关联结构上的表示学习
      • 超图上的表示学习
        • 定义
        • 结构构建
        • 基于谱域的学习
        • 基于空域的学习
    • 如何加入DHG贡献团队
      • 编程风格
      • 代码测试
      • 构建文档
  • 代码样例
    • 节点分类
      • 图
        • 模型
        • 数据集
        • 结果汇总
        • Cora上使用GCN
        • Cora上使用GAT
        • Cora上使用HGNN
        • Cora上使用HGNN+
      • 超图
        • 模型
        • 数据集
        • 结果汇总
        • Cooking200上使用GCN
        • Cooking200上使用HGNN
        • Cooking200上使用HGNN+
    • <用户-物品>二分图上的推荐
      • 模型
      • 数据集
      • 结果汇总
      • 在Gowalla上使用NGCF
        • 导入依赖包
        • 定义函数
        • 主函数
        • 输出
      • 在Gowalla上使用LightGCN
        • 导入依赖包
        • 定义函数
        • 主函数
        • 输出
    • 自动化超参调优
      • 在Cora上使用GCN
        • 配置
        • 导入依赖包
        • 定义函数
        • 主函数
        • 输出
      • 在Cooking200上使用HGNN+
        • 配置
        • 导入依赖包
        • 定义函数
        • 主函数
        • 输出
      • 在Gowalla上使用LightGCN
        • 配置
        • 导入依赖包
        • 定义函数
        • 主函数
        • 输出
  • 相关教程
    • 总览
      • 准备工作
      • 训练和评测
      • 其他
    • 构建关联结构
      • 构建低阶关联结构
        • 构建图
        • 构建有向图
        • 构建二分图
      • 构建高阶关联结构
        • 构建超图
    • 构建输入数据
      • 使用方法
      • 模块架构设计
      • 建立自己的数据集
        • 图数据集示例
        • 超图数据集示例
        • <用户-物品>二分图示例
    • 构建模型
      • 构建基于谱域的模型
      • 构建基于空域的模型
      • 构建混合操作模型
      • 构建混合关联结构模型
    • 构建指标评测器
      • 初始化
      • 整轮评测
      • 添加批数据后整轮评测
    • 训练模型
      • 没有批数据的模型的训练
      • 有批数据的模型的训练
    • 自动化超参调优
      • 自动调优的构造函数
        • 定义结构调优构造函数
        • 定义模型调优构造函数
        • 定义训练调优构造函数
      • 自动化调优的任务类
      • 自动化节点分类任务
        • 自动化图节点分类任务
        • 自动化超图节点分类任务
      • 自动化物品推荐任务
    • 随机结构生成
      • 随机图生成
      • 随机有向图生成
      • 随机二分图生成
      • 随机超图生成

API Reference

  • dhg
    • Load Structure
    • Low-Order Structures
      • Base Class
      • Graph
      • Directed Graph
      • Bipartite Graph
    • High-Order Structures
      • Base Class
      • Hypergraph
  • dhg.nn
    • Common Layers
      • MLP
    • Layers on Low-Order Structures
      • GCNConv
      • GraphSAGEConv
      • GATConv
      • GINConv
    • Layers on High-Order Structures
      • HGNNConv
      • HGNNPConv
      • JHConv
      • HyperGCNConv
      • HNHNConv
    • Loss Functions
      • BPRLoss
    • Regularizations
      • EmbeddingRegularization
  • dhg.models
    • Models on Low-Order Structures
      • GCN
      • GraphSAGE
      • GAT
      • GIN
      • NGCF
      • LightGCN
    • Models on High-Order Structures
      • HGNN
      • HGNNP
      • HyperGCN
      • HNHN
      • DHCF
  • dhg.data
    • Base Class
    • Vertex Classification Datasets
      • Cora
      • Pubmed
      • Citeseer
      • Cooking200
    • User-Item Recommender Datasets
      • MovieLens1M
      • AmazonBook
      • Yelp2018
      • Gowalla
  • dhg.datapipe
    • Compose Datapipes
    • Transforms
    • Loaders
  • dhg.metrics
    • Basic Metrics
      • Classification
      • Recommender
      • Retrieval
    • Evaluators for Different Tasks
      • Base Class
      • Vertex Classification Task
        • On Graph
        • On Hypergraph
      • Recommender Task
        • On User-Item Bipartite Graph
  • dhg.experiments
    • Base Class
    • Vertex Classification Task
      • On Graph
      • On Hypergraph
    • Recommender Task
      • On User-Item Bipartite Graph
  • dhg.random
    • Random Seed
    • Generate Graph
    • Generate Directed Graph
    • Generate Bipartite Graph
    • Generate Hypergraph
  • dhg.utils
    • Logging
    • Download
    • Structure Helpers
    • Dataset Wrapers
DHG
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  • 相关教程 »
  • 总览
  • Edit on GitHub

总览

准备工作

  1. 针对你的任务构建关联结构.

  2. 针对你的任务构建输入数据.

  3. 针对你的任务构建模型.

  4. 针对你的任务构建指标度量器.

训练和评测

  1. 构建你的训练和评测代码.

  2. 自动超参调优.

其他

  • 随机关联结构的生成.

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