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

Building a GDS Pipeline for Node Classification Model Training

Classifying observations within categories is a classical machine learning (ML) task. As we learned in the preceding chapters, we can use existing ML models such as decision trees to classify a graph’s nodes. The graph structure is used to find extra features, bringing more knowledge into the model. In this chapter, we will discover another key feature of the Neo4j GDS library: pipelines. They let you configure and train an ML model, before using it to make predictions on unseen nodes. You can do all of this from Neo4j, without having to add another library such as scikit-learn to the tech stack.

Also, we are going to work on the Netflix dataset we created earlier in this book (the code is available on GitHub if you don’t have it yet). We will try and make predictions by building a node classification pipeline, focusing on the how rather than the why.

In this chapter, we’re going to cover the...