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Graph Machine Learning
Graph Machine Learning

Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms

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Profile Icon Claudio Stamile Profile Icon Aldo Marzullo Profile Icon Enrico Deusebio
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$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (22 Ratings)
Paperback Jun 2021 338 pages 1st Edition
eBook
$42.99
Paperback
$52.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon Claudio Stamile Profile Icon Aldo Marzullo Profile Icon Enrico Deusebio
Arrow right icon
$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1 (22 Ratings)
Paperback Jun 2021 338 pages 1st Edition
eBook
$42.99
Paperback
$52.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$42.99
Paperback
$52.99
Subscription
Free Trial
Renews at $12.99p/m

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Key benefits

  • Implement machine learning techniques and algorithms in graph data
  • Identify the relationship between nodes in order to make better business decisions
  • Apply graph-based machine learning methods to solve real-life problems

Description

Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You’ll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you’ll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You’ll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.

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.

What you will learn

  • Write Python scripts to extract features from graphs
  • Distinguish between the main graph representation learning techniques
  • Learn how to extract data from social networks, financial transaction systems, for text analysis, and more
  • Implement the main unsupervised and supervised graph embedding techniques
  • Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more
  • Deploy and scale out your application seamlessly

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 25, 2021
Length: 338 pages
Edition : 1st
Language : English
ISBN-13 : 9781800204492
Category :
Languages :

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Product Details

Publication date : Jun 25, 2021
Length: 338 pages
Edition : 1st
Language : English
ISBN-13 : 9781800204492
Category :
Languages :

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Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1
(22 Ratings)
5 star 45.5%
4 star 36.4%
3 star 9.1%
2 star 0%
1 star 9.1%
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Carlo Estopia Feb 18, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Mohit Jul 27, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This books fill a critical void in an emerging field of graphs and ML. The latest research done in field of GNN, graph embeddings to more application of graphs (like NLP, SNAs etc).Easy to read with code walkthroughs helps in understanding a complicated concept.Overall, really great book.
Amazon Verified review Amazon
S M Aug 29, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is easy to read and well-written. It starts with basic concepts and ends with more advanced ones. It is also very comprehensive, covering unsupervised methods to use cases for social networks, natural language processing, and financial transactions. There's code available to see how to apply the knowledge imparted in every chapter and visuals to assist with its understanding.
Amazon Verified review Amazon
sean debusschere Feb 21, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I did research in 2017 presented at OHBM in topological data analysis in FMRI data and it's exciting to see both get mentioned at the end of the book.
Amazon Verified review Amazon
Pranjalya Tiwari Jul 26, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is very well written and provides a good depth of knowledge regarding Machine Learning on Graphs, which is relatively less explored fields than the other fields of ML and DL. The code snippets are bug free, and the authors provide in-depth analysis behind the code written. I would recommend this book to anyone who is starting on Graph Machine Learning, or even who wants to start working on Social Network analysis, or anyone who wants to gain more knowledge in this domain. Kudos to the writers for this book. Waiting for more from them.
Amazon Verified review Amazon
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