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

This chapter was a long one, as it was our introduction to the GDS plugin. It is important to understand how to define the projected graph and the different entities to be included in it. We will see more examples in the following chapters, as we are going to use this library in all remaining chapters of the book.

The following table summarizes the different algorithms we have studied in this chapter, with some important characteristics to keep in mind:

Algorithm Description Stream/Write Negative weights
shortestPath The shortest path between two nodes using Dijkstra's algorithm Both No
shortestPath.astar The shortest path between two nodes using the A* algorithm and great circle heuristics (requires nodes with latitude and longitude properties) Stream No
kShortestPath The k-shortest paths between two nodes using Yen's algorithm Both Yes
shortestPath.deltaStepping Single source shortest path: the shortest path between a node and all other nodes in the graph...