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

Importing Data into Neo4j to Build a Knowledge Graph

As discussed in the previous chapter, you do not have to fetch a graph dataset specifically to work with a graph database. Many datasets we are used to working with as data scientists contain information about the relationships between entities, which can be used to model this dataset as a graph. In this chapter, we will discuss one such example: a Netflix catalog dataset. It contains movies and series available on the streaming platform, including some metadata, such as director or cast.

First, we are going to study this dataset, which is saved as a CSV file, and learn how to import CSV data into Neo4j. In a second exercise, we will use the same dataset, stored as a JSON file. Here, we will have to use the Awesome Procedures on Cypher (APOC) Neo4j plugin to be able to parse this data and import it into Neo4j.

Finally, we will learn about the existing public knowledge graphs, one of the biggest ones being Wikidata. We will...