Book Image

Graph Data Science with Neo4j

By : Estelle Scifo
5 (1)
Book Image

Graph Data Science with Neo4j

5 (1)
By: Estelle Scifo

Overview of this book

Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.
Table of Contents (16 chapters)
1
Part 1 – Creating Graph Data in Neo4j
4
Part 2 – Exploring and Characterizing Graph Data with Neo4j
8
Part 3 – Making Predictions on a Graph

Introducing the Pregel API

Neo4j allows you to write plugins: following some API, you can write code in Java, which can then be used from Cypher through, for instance, the CALL statement, given that the JAR file containing your code has been placed in the plugins folder and your code has been properly annotated so that Neo4j can find the relevant information. That’s how APOC and GDS are implemented. Thanks to the Pregel API, we can extend not only Neo4j but GDS itself, leveraging its main features.

In this section, we will cover these GDS features and the basic principles behind the Pregel API.

GDS’s features

GDS allows users to extend it while taking advantage of many common functionalities, such as the following:

  • In-memory projected graph: We won’t have to write code to create a projected graph – we can directly work on an existing projected graph in the GDS graph catalog.
  • Stream/write/mutate procedures: The execution modes are automatically...