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Graph Neural Networks in Action
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Graph Neural Networks in Action

by Keita Broadwater and Namid Stillman

Artificial Intelligence

Book Details

Book Title

Graph Neural Networks in Action

Author

Keita Broadwater and Namid Stillman

Publisher

Manning Publications Co

Publication Date

2025

ISBN

9781617299056

Number of Pages

625

Language

English

Format

PDF

File Size

6.2MB

Subject

Computers>Algorithms and Data Structures: Pattern Recognition

Table of Contents

  • Graph Neural Networks in Action
  • copyright
  • contents
  • dedication
  • foreword
  • preface
  • acknowledgments
  • about this book
  • about the authors
  • about the cover illustration
  • Part 1 First steps
  • 1 Discovering graph neural networks
  • 1.1 Goals of this book
  • 1.1.1 Catching up on graph fundamentals
  • 1.2 Graph-based learning
  • 1.2.1 What are graphs?
  • 1.2.2 Different types of graphs
  • 1.2.3 Graph-based learning
  • 1.2.4 What is a GNN?
  • 1.2.5 Differences between tabular and graph data
  • 1.3 GNN applications: Case studies
  • 1.3.1 Recommendation engines
  • 1.3.2 Drug discovery and molecular science
  • 1.3.3 Mechanical reasoning
  • 1.4 When to use a GNN?
  • 1.4.1 Implicit relationships and interdependencies
  • 1.4.2 High dimensionality and sparsity
  • 1.4.3 Complex, nonlocal interactions
  • 1.5 Understanding how GNNs operate
  • 1.5.1 Mental model for training a GNN
  • 1.5.2 Unique mechanisms of a GNN model
  • 1.5.3 Message passing
  • 2 Graph embeddings
  • 2.1 Creating embeddings with Node2Vec
  • 2.1.1 Loading data, setting parameters, and creating embeddings
  • 2.1.2 Demystifying embeddings
  • 2.1.3 Transforming and visualizing the embeddings
  • 2.1.4 Beyond visualization: Applications and considerations of N2V embeddings
  • 2.2 Creating embeddings with a GNN
  • 2.2.1 Constructing the embeddings
  • 2.2.2 GNN vs. N2V embeddings
  • 2.3 Using node embeddings
  • 2.3.1 Data preprocessing
  • 2.3.2 Random forest classification
  • 2.3.3 Embeddings in an end-to-end model
  • 2.4 Under the Hood
  • 2.4.1 Representations and embeddings
  • 2.4.2 Transductive and inductive methods
  • 2.4.3 N2V: Random walks across graphs
  • 2.4.4 Message passing as deep learning
  • Part 2 Graph neural networks
  • 3 Graph convolutional networks and GraphSAGE
  • 3.1 Predicting consumer product categories
  • 3.1.1 Loading and processing the data
  • 3.1.2 Creating our model classes
  • 3.1.3 Model training
  • 3.1.4 Model performance analysis
  • 3.1.5 Our first product bundle
  • 3.2 Aggregation methods
  • 3.2.1 Neighborhood aggregation
  • 3.2.2 Advanced aggregation tools
  • 3.2.3 Practical considerations in applying aggregation
  • 3.3 Further optimizations and refinements
  • 3.3.1 Dropout
  • 3.3.2 Model depth
  • 3.3.3 Improving the baseline model’s performance
  • 3.3.4 Revisiting the Marcelina product bundle
  • 3.4 Under the hood
  • 3.4.1 Convolution methods
  • 3.4.2 Message passing
  • 3.4.3 GCN aggregation function
  • 3.4.4 GCN in PyTorch Geometric
  • 3.4.5 Spectral vs. spatial convolution
  • 3.4.6 GraphSAGE aggregation function
  • 3.4.7 GraphSAGE in PyTorch Geometric
  • 3.5 Amazon Products dataset
  • 4 Graph attention networks
  • 4.1 Detecting spam and fraudulent reviews
  • 4.2 Exploring the review spam dataset
  • 4.2.1 Explaining the node features
  • 4.2.2 Exploratory data analysis
  • 4.2.3 Exploring the graph structure
  • 4.2.4 Exploring the node features
  • 4.3 Training baseline models
  • 4.3.1 Non-GNN baselines
  • 4.3.2 GCN baseline
  • 4.4 Training GAT models
  • 4.4.1 Neighborhood loader and GAT models
  • 4.4.2 Addressing class imbalance in model performance
  • 4.4.3 Deciding between GAT and XGBoost
  • 4.5 Under the hood
  • 4.5.1 Explaining attention and GAT models
  • 4.5.2 Over-smoothing
  • 4.5.3 Overview of key GAT equations
  • 5 Graph autoencoders
  • 5.1 Generative models: Learning how to generate
  • 5.1.1 Generative and discriminative models
  • 5.1.2 Synthetic data
  • 5.2 Graph autoencoders for link prediction
  • 5.2.1 Review of the Amazon Products dataset from chapter 3
  • 5.2.2 Defining a graph autoencoder
  • 5.2.3 Training a graph autoencoder to perform link prediction
  • 5.3 Variational graph autoencoders
  • 5.3.1 Building a variational graph autoencoder
  • 5.3.2 When to use a variational graph autoencoder
  • 5.4 Generating graphs using GNNs
  • 5.4.1 Molecular graphs
  • 5.4.2 Identifying new drug candidates
  • 5.4.3 VGAEs for generating graphs
  • 5.4.4 Generating molecules using a GNN
  • 5.5 Under the hood
  • 5.5.1 Understanding link prediction tasks
  • 5.5.2 The inner product decoder
  • Part 3 Advanced topics
  • 6 Dynamic graphs: Spatiotemporal GNNs
  • 6.1 Temporal models: Relations through time
  • 6.2 Problem definition: Pose estimation
  • 6.2.1 Setting up the problem
  • 6.2.2 Building models with memory
  • 6.3 Dynamic graph neural networks
  • 6.3.1 Graph attention network for dynamic graphs
  • 6.4 Neural relational inference
  • 6.4.1 Encoding pose data
  • 6.4.2 Decoding pose data using a GRU
  • 6.4.3 Training the NRI model
  • 6.5 Under the hood
  • 6.5.1 Recurrent neural networks
  • 6.5.2 Temporal adjacency matrices
  • 6.5.3 Combining autoencoders with RNNs
  • 6.5.4 Gumbel-Softmax
  • 7 Learning and inference at scale
  • 7.1 Examples in this chapter
  • 7.1.1 Amazon Products dataset
  • 7.1.2 GeoGrid
  • 7.2 Framing problems of scale
  • 7.2.1 Root causes
  • 7.2.2 Symptoms
  • 7.2.3 Crucial metrics
  • 7.3 Techniques for tackling problems of scale
  • 7.3.1 Seven techniques
  • 7.3.2 General Steps
  • 7.4 Choice of hardware configuration
  • 7.4.1 Types of hardware choices
  • 7.4.2 Choice of processor and memory size
  • 7.5 Choice of data representation
  • 7.6 Choice of GNN algorithm
  • 7.6.1 Time and space complexity
  • 7.7 Batching using a sampling method
  • 7.7.1 Two concepts: Mini-batching and sampling
  • 7.7.2 A glance at notable PyG samplers
  • 7.8 Parallel and distributed processing
  • 7.8.1 Using distributed data parallel
  • 7.8.2 Code example for DDP
  • 7.9 Training with remote storage
  • 7.9.1 Example
  • 7.10 Graph coarsening
  • 7.10.1 Example
  • 8 Considerations for GNN projects
  • 8.1 Data preparation and project planning
  • 8.1.1 Project definition
  • 8.1.2 Project objectives and scope
  • 8.2 Designing graph models
  • 8.2.1 Get familiar with the domain and use case
  • 8.2.2 Constructing the graph dataset and schemas
  • 8.2.3 Creating instance models
  • 8.2.4 Testing and refactoring
  • 8.3 Data pipeline example
  • 8.3.1 Raw data
  • 8.3.2 The ETL step
  • 8.3.3 Data exploration and visualization
  • 8.3.4 Preprocessing and loading data into PyG
  • 8.4 Where to find graph data
  • Appendix A Discovering graphs
  • A.1 Graph fundamentals
  • A.1.1 Graph properties
  • A.1.2 Characteristics of nodes and edges
  • A.1.3 Categories of graphs
  • A.2 Graph representations
  • A.2.1 Basic graph data structures
  • A.2.2 Relational databases
  • A.2.3 How graphs are exposed
  • A.3 Graph systems
  • A.3.1 Graph databases
  • A.3.2 Graph compute engines (or graph frameworks)
  • A.3.3 Visualization libraries
  • A.3.4 GNN libraries
  • A.4 Graph algorithms
  • A.4.1 Traversal and search algorithms
  • A.4.2 Shortest path
  • A.5 How to read GNN literature
  • A.5.1 Common graph notations
  • Appendix B Installing and configuring PyTorch Geometric
  • B.1 Installing PyTorch Geometric
  • B.1.1 On Windows/Linux
  • B.1.2 On MacOS
  • B.1.3 Compatibility issues
  • further reading
  • references