Book

Graph Machine Learning

Data scientists working with network data will be able to put their knowledge to work with this practical guide to building machine learning algorithms using graph data. The book provides a hands-on approach to implementation and associated methodologies that will have you up and running and productive in no time.

Offered byPackt Logo

Difficulty Level

Intermediate

Completion Time

11h16m

Language

English

About Book

Who Is This Book For?

This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You’ll also need intermediate-level Python programming knowledge to get started with this book.

Book content

chapters 11h16m total length

Getting Started with Graphs

Graph Machine Learning

Unsupervised Graph Learning

Supervised Graph Learning

Problems with Machine Learning on Graphs

Social Network Graphs

Text Analytics and Natural Language Processing Using Graphs

Graph Analysis for Credit Card Transactions

Building a Data-Driven Graph-Powered Application

Novel Trends on Graphs

Related Resources

Access Ready-to-Use Books for Free!

Get instant access to a library of pre-built books—free trial, no credit card required. Start training your team in minutes!

No credit card required