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, you have learned how spatial data is stored in information systems, and how to use it with Neo4j. You now know how to use the Neo4j built-in point type and perform distance queries using it. You have also learned about the neo4j-spatial plugin, allowing more complex operations such as geometry intersection and within distance queries.

Finally, you have learned how to build a graph-based routing engine using spatial data and the shortest path algorithms implemented in the GDS library. With the help of some JavaScript code, you have even been able to nicely display the result of the path finding algorithm on a map.

In the next chapter, you will discover a new type of algorithm: centrality algorithms. They are used to quantify the node importance, depending on your definition of importance.