Graph Data Science with Neo4j: Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python...

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Estelle Scifo

Graph Data Science with Neo4j

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

Estelle Scifo

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English

Packt Publishing Limited
31 January 2023

Discrete mathematics; Combinatorics & graph theory; Programming & scripting languages: general

Supercharge your data with the limitless potential of Neo4j 5, the premier graph database for cutting-edge machine learning

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

Extract meaningful information from graph data with Neo4j's latest version 5 Use Graph Algorithms into a regular Machine Learning pipeline in Python Learn the core principles of the Graph Data Science Library to make predictions and create data science pipelines.

Book DescriptionNeo4j, 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.

What you will learn

Use the Cypher query language to query graph databases such as Neo4j Build graph datasets from your own data and public knowledge graphs Make graph-specific predictions such as link prediction Explore the latest version of Neo4j to build a graph data science pipeline Run a scikit-learn prediction algorithm with graph data Train a predictive embedding algorithm in GDS and manage the model store

Who this book is forIf you’re a data scientist or data professional with a foundation in the basics of Neo4j and are now ready to understand how to build advanced analytics solutions, you’ll find this graph data science book useful. Familiarity with the major components of a data science project in Python and Neo4j is necessary to follow the concepts covered in this book.

By: Estelle Scifo
Imprint: Packt Publishing Limited
Country of Publication: United Kingdom
Dimensions: Height: 93mm, Width: 75mm,
ISBN: 9781804612743
ISBN 10: 180461274X
Pages: 288
Publication Date: 31 January 2023
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.