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 learned about how to use GDS pipelines to simplify the processes of training an ML model involving graph-based features. GDS pipelines can be configured to run graph algorithms such as the Louvain algorithm and use the result as a feature in a classification or regression model. These models are part of the GDS, so we do not have to explicitly extract data from Neo4j and use another ML library. Everything can be run using the projected graph, which is stored in the model and pipeline catalogs, and used to make predictions on unseen nodes. This lets us use a single tool to compute graph features and perform ML tasks, including the training and prediction of different models, without explicit data exchange from and to the database.

Additionally, we played with the embedding algorithms included in the GDS, starting to surface their advantages and disadvantages.

In the next chapter, we will use another type of pipeline from the GDS to solve another kind...