Discover our recommended Graph Database and Analytics reads below. Learn how to build intelligent platforms with native graph performance, find patterns and hidden connections in your data, navigate large hierarchies and multi-level data, and more. Some are authored by us, but in the spirit of community we are also including content authored by others.

If you would like to submit your work for consideration please contact devrel@neo4j.com

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Essential GraphRAG

By Tomaž Bratanic and Oskar Hane

Publisher: Manning

Check out this a comprehensive guide from Manning on how to build a GraphRAG system from scratch. 

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Graph Data Processing with Cypher

By Ravindranatha Anthapu

Publisher: Packt

Graph Data Processing with Cypher: A practical guide to building graph traversal queries using the Cypher syntax on Neo4j.

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Graph Data Science with Neo4j

By Estelle Scifo

Publisher: Packt

Graph Data Science library 2.0 and its Python driver for your project.

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Visual Design of GraphQL Data: A Practical Introduction with Legacy Data and Neo4j

By Thomas Frisendal

Publisher: Apress

Get an introduction to the visual design of GraphQL data and concepts, including GraphQL structures, semantics, and schemas in this compact, pragmatic book. In it you will see simple guidelines based on lessons learned from real-life data discovery and unification,… Read more →

Building Knowledge Graphs: A Practitioner’s Guide

This guide is a crucial resource for developers and data scientists who aspire to excel in building, managing, and leveraging knowledge graphs, brought to you by Neo4j and O’Reilly. It covers everything from the basics to advanced approaches, equipping you with the knowledge and technical skills to employ this groundbreaking technology in your projects. Get your complimentary ebook today, a $90 value in stores, for free.

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Fullstack GraphQL Applications with React, Node.js, and Neo4j

Interested in building full-stack applications? Full Stack GraphQL Applications teaches you how to use GraphQL, React, Node.js, and Neo4j to build and deploy complex, data-intensive full-stack applications. In this book, author Will Lyon walks you step by step through creating and deploying an application in the cloud.

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Graph Data Science For Dummies, Second Edition

Graph Data Science For Dummies, Second Edition focuses on the applications of graph analysis and graph-enhanced machine learning, which both take the form of graph data science. You discover graph data science basics and learn about its adoption. We use the Neo4j database technology to help illustrate our points about the graph data science platform. We also supply you with plenty of resources to guide you outside of what this introductory book provides.

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Beginning Neo4j

This book is your introduction in the world of graph databases, and the benefits they can bring to your applications. Neo4j is the most established graph database on the market, and it’s always improving to bring more of its benefits to you.

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Building Neo4j-Powered Applications with LLMs

Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI

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Building Web Applications with Python and Neo4j

Develop exciting real-world Python-based web applications with Neo4j using frameworks such as Flask, Py2neo, and Django

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Connected

Renowned scientists Christakis and Fowler present compelling evidence for our profound influence on one another’s tastes, health, wealth, happiness, beliefs, even weight, as they explain how social networks form and how they operate.

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Good Relationships

This guide introduces you to Spring Data Neo4j, using the fast, powerful and scalable graph database Neo4j to enjoy the benefits of having good relationships in your data.

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Graph Algorithms for Data Science

Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

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