You're reading from Graph Data Science with Neo4j Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project

Product type Paperback

Published in Jan 2023

Publisher Packt

ISBN-13 9781804612743

Length 288 pages

Edition 1st Edition

Languages

Python

Tools

Neo4j

Concepts

Data Science

Author (1):

Estelle Scifo

Preface

1. Part 1 – Creating Graph Data in Neo4j

2. Chapter 1: Introducing and Installing Neo4j FREE CHAPTER

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

15. Other Books You May Enjoy

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 Chapter 1, Introducing and Installing Neo4j).
The necessary Python and Jupyter notebooks installed. We are not going to cover the installation instructions in this book.
You’ll also need the following Python packages:
- matplotlib
- pandas
- neo4j
An internet connection to download the plugins and the dataset and to use the public API in the last section of this chapter.
Any code listed in the book will be available in the associated GitHub repository, https://github.com/PacktPublishing/Graph-Data-Science-with-Neo4j, in the corresponding chapter folder.

The rest of the chapter is locked