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

Summary

In this chapter, we discussed how to build a web application using Neo4j as the main database. You should now be able to build a web application backed by Neo4j using either Python, its neomodel package, and the Flask framework to build a full-stack web application (back and frontend); GraphQL, to build an API out of Neo4j that can be plugged to any existing frontend; or GRANDstack, which allows you to create a frontend application for retrieving data from Neo4j using a GraphQL API.

Even though we have specifically addressed the concepts of users and repositories, this knowledge can be extended to any other type of object and relationship pretty easily; for example, repositories can become products, movies, or posts written by the user. If you have used a link prediction algorithm to build a followers recommendation engine, as we did in Chapter 9, Predicting Relationships, you can use the knowledge you've gained in this chapter to show a list of recommended users to follow...