Preface

Organizations across the globe are starting to use graph approaches and visualization techniques to make sense of complex networks. These networks are present in many industries, ranging from social network analysis (analyzing the connections of people interacting on social networks) to fraud detection (looking at transactions in a network to spot outliers), modeling the stability of systems such as rail and energy grids, and as critical components of recommendation engines that are used in many of your favorite online streaming services, for example, Netflix, Prime, and so on.

This book provides you with the tools to get up and running with these methods while working with a familiar language, such as Python. We start by looking at how you can create graphs in igraph NetworkX and how these can be used to carry out sophisticated graph analytics. We will then delve into the world of Neo4j and graph databases, as well as equipping you with the knowledge to query graph databases with the Cypher query language.