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

This is the end of this chapter, where you were introduced to the method you can use to extend GDS and take advantage of all the common features we are looking for when dealing with graph analytics: memory and CPU performance. The projected graph and GDS internal management of job batches are easily accessible to us if we write a couple of Java classes.

We also studied the PageRank algorithm and implemented two versions of it: one relying only on the maximum number of iterations as stopping criteria, and another version that considers the stability of computed scores compared to the previous iteration, within a certain tolerance. We also learned how to unit test our algorithm by writing a simple test that runs our algorithm on a sample graph, which we were able to define by writing a Cypher CREATE statement.

This chapter is also the end of this book! We have come a long way since Chapter 1, Introducing and Installing Neo4j, where we introduced the concept of graphs, and...