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.


Contents and Chapter Drafts

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:

author={Hamilton, William L.},
title={Graph Representation Learning},
journal={Synthesis Lectures on Artificial Intelligence and Machine Learning},
publisher={Morgan and Claypool}

All copyrights held by the author and publishers extend to the pre-publication drafts.


Feedback, typo corrections, and comments are welcome and should be sent to with [GRL BOOK] in the subject line.