Book Image

Graph Data Science with Neo4j

By : Estelle Scifo
5 (1)
Book Image

Graph Data Science with Neo4j

5 (1)
By: Estelle Scifo

Overview of this book

Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.
Table of Contents (16 chapters)
1
Part 1 – Creating Graph Data in Neo4j
4
Part 2 – Exploring and Characterizing Graph Data with Neo4j
8
Part 3 – Making Predictions on a Graph

Technical requirements

To be able to reproduce the examples provided in this chapter, you’ll need the following tools:

  • Neo4j installed on your computer (see the installation instructions in the previous chapter).
  • The Neo4j APOC plugin (the installation instructions will be provided later in this chapter, in the Introducing the APOC library to deal with JSON data section).
  • Python and the Jupyter Notebook installed. We are not going to cover the installation instructions in this book. However, they are detailed in the code repository associated with this book (see the last bullet if you need such details).
  • An internet connection to download the plugins and the datasets. This will also be required for you to use the public API in the last section of this chapter (Enriching our graph with Wikidata information).
  • Any code listed in this book will be available in the associated GitHub repository, https://github.com/PacktPublishing/Graph-Data-Science-with-Neo4j...