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.
- Download the pre-publication pdf.
- Access the individual chapters below.
- Print and e-book editions will be released by Morgan & Claypool publishers in late 2020.
Contents and Chapter Drafts
- Chapter 1: Introduction and Motivations [Draft. Updated August 2020.]
- Chapter 2: Background and Traditional Approaches [Draft. Updated August 2020.]
- Part I: Node Embeddings
- Part II: Graph Neural Networks
- Part III: Generative Graph Models
- Bibliography [Draft. Updated August 2020.]
Copyrights and Citation
This book is a pre-publication draft of a book that will be published by Morgan & Claypool publishers in late 2020, and the publishers have generously agreed to allow the public hosting of the pre-publication draft. The book should be cited as follows:
William L. Hamilton. (2020). Graph Representation Learning. Morgan & Claypool, forthcoming .
All copyrights held by the author and publishers extend to these pre-publication drafts.
Feedback, typo corrections, and comments are welcome and should be sent to firstname.lastname@example.org with [GRL BOOK] in the subject line.