Key Features
Learn how to deal with a graph database
Extract meaningful information from graph data
Use Graph Algorithms into a regular Machine Learning pipeline in Python
Book Description
Neo4j and its Graph Data Science Library is a complete solution to store, query and analyze graph data. Graph databases are getting more popular among developers, which means data scientists are likely to face such databases in their future career. Moreover, graph algorithms are a trending topic which enable extracting context information and improve 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 its Graph Data Science Library. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running. Complete with step-by-step explanations of concepts and practical examples. You will begin by querying Neo4j with Cypher and characterize graph datasets. You'll learn how to run graph algorithms on graph data stored into Neo4j, understand the core principles of the Graph Data Science Library to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you will be able to include graph algorithms into your normal ML pipeline. By the end of this book, you will be able to take advantage of the relationships in your dataset to improve your current model and make other types of prediction.
What you will learn
Querying graph databases such as Neo4j using the Cypher query language
Build graph datasets from your own data and public knowledge graphs
Extract new kind of features thanks by connecting observations
Make graph-specific predictions such as link prediction
Build a graph data science pipeline with Neo4j
Who This Book Is For
Data Scientists and data professionals who have learnt the basics of Neo4j and now want to understand how to build advanced analytics solutions will find this graph data science book useful. Familiarity with the major components of a Data Science project in Python and Neo4J is required.
Table of Contents
Introducing and Installing Neo4j
Using existing data to build a Knowledge Graph
Characterizing a Graph Dataset
Using Graph Algorithms to Characterize a Graph Dataset
Visualizing Graph Data
Building a Machine Learning Model with Graph Features
Automatically Extracting Features with Graph Embeddings for Machine Learning
Building a GDS Pipeline for Node Classification Model Training
Predicting Future Edges
Writing your custom graph algorithm with the Pregel API