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This book has been written for anyone with no experience with graphs to more experienced graph users to augment their understanding of graph algorithms and their role in the various analysis. With traditional analytics, you are trying to make sense of data points, whereas with graph analytics, you are more focused on analyzing connections between data points. Graph algorithms are designed to investigate those connections between data points and help you explore who is well connected, who has the most influence, how communities form, and more. When I first started experimenting with graph analytics five years ago, there weren’t many tools available, and you would often need to combine a few tools helping you construct a graph and then analyze it. Not only that, but there weren’t many tutorials or courses available that would explain the graph analysis workflow in simple terms. For this reason, I’ve decided to write this book that would minimize the number of tools needed to get started and explain graph algorithms and analysis workflow in basic terms. I will present the advantages of using a graph database and teach you how to construct a graph from structured and unstructured data using NLP techniques. Along the way, you will learn how to use Cypher query language to manipulate graph structure and extract valuable insights. Next, I will walk you through the typical graph algorithms like PageRank and community detection/clustering algorithms and demonstrate how to use them in practice. The last part of the book will focus on graph machine learning, specifically how to translate graph topology and structure into machine learning model input by using node embedding models and graph neural networks.