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

Exercises

To make sure you fully understand the content described in this chapter, you are encouraged to think about the following exercises before moving on:

Which information do you need to define a graph?
Do you need a graph dataset to start using a graph database?
True or false:
1. Neo4j can only be started with Neo4j Desktop.
2. The application to use to create dashboards from Neo4j data is Neo4j Browser.
3. Graph data science is supported by default by Neo4j.
Are the following Cypher syntaxes valid, and why/why not? What are they doing?
1. MATCH (x:User) RETURN x.name
2. MATCH (x:User) RETURN x
3. MATCH (:User) RETURN x.name
4. MATCH (x:User)-[k:KNOWS]->(y:User) RETURN x, k, y
5. MATCH (x:User)-[:KNOWS]-(y) RETURN x, y
Create more data (other node labels/relationship types) and queries.