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

To get the most out of this book

You will need access to a Neo4j instance. Options and installation instructions are given in Chapter 1, Introducing and Installing Neo4j. We will also intensively use Python and the following packages: pandas, scikit-learn, network, and graphdatascience. The code was tested with Python 3.10 but should work with newer versions, assuming no breaking change is made in its dependencies. Python code is provided as a Jupyter notebook, so you’ll need Jupyter Server installed and running to go through it.

For the very last chapter, a Java JDK will also be required. The code was tested with OpenJDK 11.

Software/hardware covered in the book

Operating system requirements

Neo4j 5.x

Windows, macOS, or Linux

Python 3.10

Windows, macOS or Linux

Jupyter

Windows, macOS or Linux

OpenJDK 11

Windows, macOS or Linux

You will also need to install Neo4j plugins: APOC and GDS. Installation instructions for Neo4j Desktop are given in the relevant chapters. However, if you are not using a local Neo4j instance, please refer to the following pages for installation instructions, especially regarding version compatibilities:

  • APOC: https://neo4j.com/docs/apoc/current/installation/
  • GDS: https://neo4j.com/docs/graph-data-science/current/installation/

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.