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 and Installing Neo4j

Graph databases in general, and Neo4j in particular, have gained increasing interest in the past few years. They provide a natural way of modeling entities and relationships and take into account observation context, which is often crucial to extract the most out of your data. Among the different graph database vendors, Neo4j has become one of the most popular for both data storage and analytics. A lot of tools have been developed by the company itself or the community to make the whole ecosystem consistent and easy to use: from storage to querying, to visualization to graph data science. As you will see through this book, there is a well-integrated application or plugin for each of these topics.

In this chapter, you will get to know what Neo4j is, positioning it in the broad context of databases. We will also introduce the aforementioned plugins that are used for graph data science.

Finally, you will set up your first Neo4j instance locally if you haven’t done so already and run your first Cypher queries to populate the database with some data and retrieve it.

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

  • What is a graph database?
  • Finding or creating a graph database
  • Neo4j in the graph databases landscape
  • Setting up Neo4j
  • Inserting data into Neo4j with Cypher, the Neo4j query language
  • Extracting data from Neo4j with Cypher pattern matching