Graph Data Science with Neo4j
The ever-increasing need of graph representation among data scientists for modeling complex relationships and extracting contextual information is addressed by the latest version of Neo4j. This book shows you how to set up a graph machine learning pipeline using Neo4j 5, its Graph Data Science Library 2.0, and its Python client.
Offered by
Difficulty Level
Intermediate
Completion Time
9h36m
Language
English
About Book
Who Is This Book 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.
Graph Data Science with Neo4j
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 9h36m total length
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
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