Many top universities make some of their courses available for free to non-students, a trend which has been gradually increasing over the years. While perhaps not the first example of such an offering, we can thank Andrew Ng (among others, certainly) for making his Stanford Machine Learning course available beyond the classroom, first via third party means, and then as one of the first courses on the MOOC platform Coursera. Since then, courses offered both via such a platform as well as those with publicly-accessible course websites have rapidly increased in number. There are no shortages of quality, free university level courses these days & mdash especially in computer science, data science, machine learning, and other tech disciplines. Right off the bat, note that when we say "free" we mean that much of a course's learning material has been made available to the masses without cost.

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. 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. Feedback, typo corrections, and comments are welcome and should be sent to wlh@cs.mcgill.ca