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Graph databases have gained increasing attention in the last few years. Data models built from graphs bring together the simplicity of document-oriented databases and the clarity of SQL tables. Among others, Neo4j is a database that comes with a large ecosystem, including the database, but also tools to build web applications, such as the GRANDstack, and tools to use graph data in a machine learning pipeline, as well as the Graph Data Science Library. This book will discuss those tools, but let's first start from the beginning.

Talking about graph databases means talking about graphs. Even if you do not need to know all the details about graph theory, it’s always a good idea to learn some of the basic concepts underlying the tool you are using. In this chapter, we will start by defining graphs and giving some simple and less simple examples of graphs and their applications. We will then see how to move from the well-known SQL tables to graph data modeling. We’ll conclude by introducing Neo4j and its building blocks, and review some design principles to understand what can and can’t be done with Neo4j.

This chapter will cover the following topics:

Graph definition and examples
Moving from SQL to graph databases
Neo4j: the nodes, relationships, and properties model
Understanding graph properties
Considerations for graph modeling in Neo4j

Key benefits

Get up and running with graph analytics with the help of real-world examples

Explore various use cases such as fraud detection, graph-based search, and recommendation systems

Get to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scaling

Description

Neo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j. By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data.

What you will learn

Become well-versed with Neo4j graph database building blocks, nodes, and relationships Discover how to create, update, and delete nodes and relationships using Cypher querying Use graphs to improve web search and recommendations Understand graph algorithms such as pathfinding, spatial search, centrality, and community detection Find out different steps to integrate graphs in a normal machine learning pipeline Formulate a link prediction problem in the context of machine learning Implement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphs

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