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

Summary

Importing existing data into a brand-new database is always a concern, as we covered in this chapter. From a flat (a non-graph format) file, you can identify node labels and relationship types between them, transforming a flat dataset into a real graph. Whether your data is stored as CSV, JSON, on your local disk or distant server, or via an API endpoint, you can now load this data into Neo4j and start exploring your graph. You also learned about the Neo4 Data Importer tool, which is used to import data stored as CSV files in a cloud-hosted Neo4j database (Aura).

You also learned about public knowledge graphs, such as Wikidata, which can be used to extend your knowledge by importing more data about a specific topic.

Finally, you learned how to import your data into the cloud thanks to the Neo4j Data Importer application.

Being able to create a graph dataset is only the beginning, though. Like any dataset, graph datasets are very different from one to another. While...