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

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Table of Contents (16) Chapters

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

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Building a Machine Learning Model with Graph Features

So far, we have explored how to build and understand a graph dataset. We have computed some key metrics, such as the degree distribution. Using unsupervised algorithms, such as community detection, we have also better-identified graph structures. Finally, the graph visualization tools have enabled us to see the content of the dataset, and visually identify some aspects of the graph. Now, it is time to start applying this knowledge to build a machine learning (ML) model. In this chapter, we will introduce the Python client for the Graph Data Science (GDS) library, by allowing it to run graph algorithms directly from Python, without writing any Cypher. After computing and extracting our features from Neo4j, we will build a scikit-learn pipeline to train a model and make predictions.

In this chapter, we’re going to cover the following main topics:

Introducing the GDS Python client
Running GDS algorithms from Python...

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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

Table of Contents (16) Chapters

Building a Machine Learning Model with Graph Features

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Authors (2)