The field of graph representation learning has grown at an incredible (and sometimes unwieldy) pace over the past seven years, transforming from a small subset of researchers working on a relatively niche topic to one of the fastest growing sub-areas of deep learning.
This book is my attempt to provide a brief but comprehensive introduction to graph representation learning, including methods for embedding graph data, graph neural networks, and deep generative models of graphs.
Access
- Download the pre-publication pdf.
- Purchase the e-book or print edition here.
- Access the individual chapters (in pre-publication form) below.
Contents and Chapter Drafts
- Chapter 1: Introduction and Motivations [Draft. Updated September 2020.]
- Chapter 2: Background and Traditional Approaches [Draft. Updated September 2020.]
- Part I: Node Embeddings
- Chapter 3: Neighborhood Reconstruction Methods [Draft. Updated September 2020.]
- Chapter 4: Multi-Relational Data and Knowledge Graphs [Draft. Updated September 2020.]
- Part II: Graph Neural Networks
- Chapter 5: The Graph Neural Network Model [Draft. Updated September 2020.]
- Chapter 6: Graph Neural Networks in Practice [Draft. Updated September 2020.]
- Chapter 7: Theoretical Motivations [Draft. Updated September 2020.]
- Part III: Generative Graph Models
- Chapter 8: Traditional Graph Generation Approaches [Draft. Updated September 2020.]
- Chapter 9: Deep Generative Models [Draft. Updated September 2020.]
- Bibliography [Draft. Updated September 2020.]
Copyrights and Citation
This book is a pre-publication draft of the book that has been published by Morgan & Claypool. The publishers have generously agreed to allow the public hosting of the pre-publication draft, which does not include the publisher's formatting or revisions. The book should be cited as follows:
@article{
author={Hamilton, William L.},
title={Graph Representation Learning},
journal={Synthesis Lectures on Artificial Intelligence and Machine Learning},
volume={14},
number={3},
pages={1-159},
publisher={Morgan and Claypool}
}
All copyrights held by the author and publishers extend to the pre-publication drafts.
Errata
Feedback, typo corrections, and comments are welcome and should be sent to wlh@cs.mcgill.ca with [GRL BOOK] in the subject line.