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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Writing Your Custom Graph Algorithms with the Pregel API in Java

In this final chapter related to creating data science projects on graphs using Neo4j and its plugins, we are going to use an advanced feature of GDS: the Pregel API. This API lets us use the optimized in-memory projected graph to run an algorithm written in Java. GDS takes care of everything else, including parallelism and how to return the result (stream or write back to Neo4j). We will use the PageRank algorithm as an example and learn about its principles before studying a small Python implementation. Then, we will implement it with the Pregel API and test our algorithm with the GDS tools. Finally, we will build the JAR file needed to run our algorithm from Cypher, like any other GDS algorithm.

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

Introducing the Pregel API
Implementing the PageRank algorithm
Testing our code
Using our algorithm from Cypher

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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

Writing Your Custom Graph Algorithms with the Pregel API in Java

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Authors (2)