Book Image

Hands-On Graph Analytics with Neo4j

By : Estelle Scifo
Book Image

Hands-On Graph Analytics with Neo4j

By: Estelle Scifo

Overview of this book

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.
Table of Contents (18 chapters)
1
Section 1: Graph Modeling with Neo4j
5
Section 2: Graph Algorithms
10
Section 3: Machine Learning on Graphs
14
Section 4: Neo4j for Production

Introducing the Graph Data Science plugin

We'll start by introducing the GDS plugin. Provided by Neo4j, it extends the capabilities of its graph database for analytics purposes. In this section, we will go through naming conventions and introduce the very important concept of graph projection, which we will use intensively in the rest of this book.

The first implementation of this plugin was done in the Graph Algorithms library, which was first released in June 2017. In 2020, it was replaced by the GDS plugin. The GDS plugin includes performance optimization for the most used algorithms so that they can run on huge graphs (several billions of nodes). Even though I will be highlighting the optimized algorithms in this book, I would suggest you refer to the latest documentation to ensure you get the most up-to-date information (https://neo4j.com/docs/graph-data-science/current/).

The full code of the GDS plugin is open source and available on GitHub at: https://github.com/neo4j/graph...