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

Exercises

To make sure you understand the topics covered in this chapter before moving on to the next one, you are encouraged to think about the following:

  1. What is the advantage of a MERGE statement over CREATE?
  2. Can raw Cypher parse JSON data? What tool should you use for that?
  3. Practice! Using the Netflix dataset, set the movie’s genres contained in the listed_in column in the CSV dataset (assume Movies has already been imported).
  4. Practice! Using the Netflix JSON dataset, write a Cypher query to import actors (assume Movies has already been imported).
  5. Knowing that a given user – let’s call her Alice – watched the movie named Confessions of an Invisible Girl, what other Netflix content can we recommend to Alice?
  6. Practice! Refine the SPARQL query we’ve built to make sure the person is an actor or movie director.

Help: You can use Robert Cullen as an example.

The answers are provided at the end of this book.

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