Graph Representation Learning Book: Detailed Introduction

Overview

This is a comprehensive textbook on graph representation learning, authored by Professor William L. Hamilton from McGill University. The book aims to provide a concise yet comprehensive introduction to graph representation learning, covering graph data embedding methods, graph neural networks, and deep generative models for graphs.

Background

The field of graph representation learning has developed at an astonishing pace over the past seven years, transforming from a relatively niche research subset into one of the fastest-growing subfields within deep learning. This book is the author's attempt to provide an authoritative introduction to this rapidly evolving domain.

Availability

• Free Version: Pre-publication PDF files are available for download.
• Official Version: The e-book or print version can be purchased through Morgan & Claypool Publishers.
• Chapter Access: Pre-publication versions of individual chapters can be accessed separately.

Content Structure

Foundations

• Chapter 1: Introduction and Motivations

  • Last updated: September 2020

• Chapter 2: Background and Traditional Approaches

  • Last updated: September 2020

Part I: Node Embeddings

• Chapter 3: Neighborhood Reconstruction Methods

  • Last updated: September 2020

• Chapter 4: Multi-Relational Data and Knowledge Graphs

  • Last updated: September 2020

Part II: Graph Neural Networks

• Chapter 5: The Graph Neural Network Model

  • Last updated: September 2020

• Chapter 6: Graph Neural Networks in Practice

  • Last updated: September 2020

• Chapter 7: Theoretical Motivations

  • Last updated: September 2020

Part III: Generative Graph Models

• Chapter 8: Traditional Graph Generation Approaches

  • Last updated: September 2020

• Chapter 9: Deep Generative Models

  • Last updated: September 2020

Appendix

• Bibliography
  • Last updated: September 2020

Academic Information

• Author: William L. Hamilton
• Publisher: Morgan & Claypool Publishers
• Series: Synthesis Lectures on Artificial Intelligence and Machine Learning
• Volume: 14
• Issue: 3
• Pages: 1-159

Copyright Information

The pre-publication version of this book is publicly accessible with the generous permission of the publisher, but does not include the publisher's formatting or revisions. All copyrights are held by the author and the publisher.

Feedback Channel

Feedback, error corrections, and comments are welcome. Please send them to wlh@cs.mcgill.ca with the subject line [GRL BOOK].

Learning Value

This book provides a valuable learning resource for the following individuals:

• Deep Learning Researchers
• Graph Neural Network Beginners
• Graph Data Analysis Professionals
• Machine Learning Engineers
• Graduate and Ph.D. Students

Core Technical Areas

• Graph Embedding Techniques
• Graph Neural Network Architectures
• Knowledge Graph Processing
• Graph Generative Models
• Deep Learning Applications on Graph Data