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

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 Description

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.

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 for

If 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.

About the Author

Estelle Scifo possesses over 7 years experience as a data scientist, after receiving her PhD from the Laboratoire de lAcclrateur Linaire, 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 beginners needs make her an excellent teacher.

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 GOS Pipeline for Node Classi fication Model Training
9. Predicting Future Edges
10. Writing Your Custom Graph Algorithm with the Pregel API