Claudio Stamile - Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
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- Book:Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
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Build machine learning algorithms using graph data and efficiently exploit topological information within your models
Key Features- 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
Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks.
You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with 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. Youll then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover 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. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to 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.
What you will learn- Write Python scripts to extract features from graphs
- Distinguish between the main graph representation learning techniques
- Become well-versed with extracting data from social networks, financial transaction systems, 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
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. The book will also be useful for machine learning developers or anyone who want to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required. Intermediate-level working knowledge of Python programming and machine learning is also expected to make the most out of this book.
Table of Contents- 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
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Take graph data to the next level by applying machine learning techniques and algorithms
Claudio Stamile
Aldo Marzullo
Enrico Deusebio
BIRMINGHAMMUMBAI
Copyright 2021 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Group Product Manager: Kunal Parikh
Publishing Product Manager: Devika Battike
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First published: May 2021
Production reference: 1270521
Published by Packt Publishing Ltd.
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ISBN 978-1-80020-449-2
www.packt.com
Alla memoria di mio Zio, Franchino Avolio. Alle ruote delle bici troppo sgonfie, all'infanzia che mi ha regalato.
In memory of my uncle, Franchino Avolio. To the wheels of bikes that are too flat, to the childhood he gave me.
Claudio Stamile
To my family, my roots.
Aldo Marzullo
To Lili, for always reminding me with your 'learning' process how wonderful the human brain and life are.
Enrico Deusebio
Claudio Stamile received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2013 and, in September 2017, he received his joint Ph.D. from KU Leuven (Leuven, Belgium) and Universit Claude Bernard Lyon 1 (Lyon, France). During his career, he has developed a solid background in artificial intelligence, graph theory, and machine learning, with a focus on the biomedical field. He is currently a senior data scientist in CGnal, a consulting firm fully committed to helping its top-tier clients implement data-driven strategies and build AI-powered solutions to promote efficiency and support new business models.
Aldo Marzullo received an M.Sc. degree in computer science from the University of Calabria (Cosenza, Italy) in September 2016. During his studies, he developed a solid background in several areas, including algorithm design, graph theory, and machine learning. In January 2020, he received his joint Ph.D. from the University of Calabria and Universit Claude Bernard Lyon 1 (Lyon, France), with a thesis entitled Deep Learning and Graph Theory for Brain Connectivity Analysis in Multiple Sclerosis. He is currently a postdoctoral researcher at the University of Calabria and collaborates with several international institutions.
Enrico Deusebio is currently the chief operating officer at CGnal, a consulting firm that helps its top-tier clients implement data-driven strategies and build AI-powered solutions. He has been working with data and large-scale simulations using high-performance facilities and large-scale computing centers for over 10 years, both in an academic and industrial context. He has collaborated and worked with top-tier universities, such as the University of Cambridge, the University of Turin, and the Royal Institute of Technology (KTH) in Stockholm, where he obtained a Ph.D. in 2014. He also holds B.Sc. and M.Sc. degrees in aerospace engineering from Politecnico di Torino.
Kacper Kubara is a technical co-founder of Artemo and a data engineer at Annual Insight, and is currently pursuing a postgraduate degree in AI at the University of Amsterdam. Despite the focus of his research being graph representation learning, he is also interested in the tools and methods that help to bridge the gap between the AI industry and academia.
Tural Gulmammadov has been leading a group of data scientists and machine learning engineers at Oracle to tackle applied machine learning problems from various industries. He is dedicated to and motivated by the applications of graph theory and discrete mathematics in machine learning over distributed computational environments. He is a cognitive science, statistics, and psychology enthusiast, as well as a chess player, painter, seasonal horse rider, and paddler.
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