Graph Data Science with Neo4j: Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project

by Estelle Scifo

Overview

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.

Product Details

ISBN-13:	9781804614907
Publisher:	Packt Publishing
Publication date:	01/31/2023
Sold by:	Barnes & Noble
Format:	eBook
Pages:	288
File size:	10 MB

About the Author

Estelle Scifo is a Neo4j Certified Professional and Neo4j Graph Data Science certified user. She is currently a machine learning engineer at GraphAware where she builds Neo4j-related solutions to make customers happy with graphs. Before that, she worked in several fields, starting out with research in particle physics, during which she worked at CERN on uncovering Higgs boson properties. She received her PhD in 2014 from the Laboratoire de l’Accélérateur Linéaire (Orsay, France). Continuing her career in industry, she worked in real estate, mobility, and logistics for almost 10 years. In the Neo4j community, she is known as the creator of neomap, a map visualization application for data stored in Neo4j. She also regularly gives talks at conferences such as NODES and PyCon. Her domain expertise and deep insight into the perspective of a beginner's needs make her an excellent teacher.
Estelle Scifo possesses over 7 years’ experience as a data scientist, after receiving her PhD from the Laboratoire de l’Accélérateur Linéaire, Orsay (affiliated to CERN in Geneva). As a Neo4j certified professional, she uses graph databases on a daily basis and takes full advantage of its features to build efficient machine learning models out of this data. In addition, she is also a data science mentor to guide newcomers into the field. Her domain expertise and deep insight into the perspective of the beginner’s needs make her an excellent teacher.

Table of Contents

Table of Contents

1. Introducing and Installing Neo4j
2. Using Existing Data to Build a Knowledge Graph
3. Characterizing a Graph Dataset
4. Using Graph Algorithms to Characterize a Graph Dataset
5. Visualizing Graph Data
6. Building a Machine Learning Model with Graph Features
7. Automatically Extracting Features with Graph Embeddings for Machine Learning
8. Building a GDS Pipeline for Node Classification Model Training
9. Predicting Future Edges
10. Writing Your Custom Graph Algorithm with the Pregel API