Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum
chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can
learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning,
including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data,
and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have
led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question
answering, and social network analysis.This book provides a synthesis and overview of graph representation learning. It begins
with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory
and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based
methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful
graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data.
The book concludes with a synthesis of recent advancements in deep generative models for graphs-a nascent but quickly growing
subset of graph representation learning.