You're reading from Graph Data Science with Neo4j

Product type Book

Published in Jan 2023

Publisher Packt

ISBN-13 9781804612743

Pages 288 pages

Edition 1st Edition

Languages

Python

Concepts

Mobile Application Development

Author (1):

Estelle Scifo

Table of Contents (16) Chapters

Preface

1. Part 1 – Creating Graph Data in Neo4j

2. Chapter 1: Introducing and Installing Neo4j

3. Chapter 2: Importing Data into Neo4j to Build a Knowledge Graph

4. Part 2 – Exploring and Characterizing Graph Data with Neo4j

5. Chapter 3: Characterizing a Graph Dataset

6. Chapter 4: Using Graph Algorithms to Characterize a Graph Dataset

7. Chapter 5: Visualizing Graph Data

8. Part 3 – Making Predictions on a Graph

9. Chapter 6: Building a Machine Learning Model with Graph Features

10. Chapter 7: Automatically Extracting Features with Graph Embeddings for Machine Learning

11. Chapter 8: Building a GDS Pipeline for Node Classification Model Training

12. Chapter 9: Predicting Future Edges

13. Chapter 10: Writing Your Custom Graph Algorithms with the Pregel API in Java

14. Index

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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:

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.