When you read this book, I hope you are as astonished by the power of relationships and connected information as I was when I first met Emil Eifrém, one of the founders of Neo4j, 15 years ago on a geek cruise on the Baltic Sea. Ten years later, a similarly inspiring and impactful meeting happened when Tomaž and I met for the first time in person in London. He’d been active in the Neo4j community for a while. After that meeting, his contributions skyrocketed, initially helping test and document the predecessor of the Graph Data Science library and at the same time becoming a prolific author on data science topics related to graphs, NLP, and their practical applications (bratanic-tomaz.medium.com). Tomaž must have published hundreds of articles by the time we were contacted by Manning to discuss creating a book on graph analytics—the one you’re holding right now. Tomaž was the obvious choice to become its author, and he did an amazing job, distilling his experience, educational writing style, and real-world examples into an insightful and entertaining book. This book is a journey into the hidden depths of connected data using graph algorithms and new ML techniques—like node embeddings—and graph machine learnings, like link prediction and node classification, many of which now find applications in areas like vector search or large-language models based on transformers like GPT.