The book "Graph Representation Learning" by William L. Hamilton provides a comprehensive introduction to the methods and techniques for learning from graph-based data. Graphs are prevalent in many fields of natural and social sciences, ranging from telecommunications networks to quantum chemistry. In recent years, research in graph-based learning has significantly increased, with new approaches such as deep graph embeddings and neural message passing methods being developed. This textbook covers the fundamental goals and methodological foundations of graph theory and network analysis, offering a detailed overview of various methods for generating node embeddings. It also introduces the concept of Graph Neural Networks (GNN), which has established itself as the dominant paradigm for deep learning with graph-based data. Finally, current advancements in deep generative models for graphs are discussed.