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

In this chapter, you have learned the basic principles of the Neo4j GDS library 2.x. You have been able to create projected graphs, configuring included nodes, relationships, and properties with native graph projection. You have also learned how to generate properties or relationships on the fly using Cypher projections. In the second section, you have run your first GDS algorithm—the degree algorithm—and got familiar with the stream, write, and mutate algorithm modes. You have also been made aware of the algorithm configuration, especially regarding relationship orientation.

Once GDS had no more secrets to you, we started using other types of algorithms—namely, community detection algorithms. We studied a few of them and learned about their differences and what they can teach us about our graph.

In the next chapter, we will learn how to use another powerful tool of the Neo4j universe: Neo4j Bloom, yet another graph application. Bloom is designed...