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Graph Machine Learning

You're reading from Graph Machine Learning

Product type Book

Published in Jun 2021

Publisher Packt

ISBN-13 9781800204492

Pages 338 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Authors (3):

Claudio Stamile

Aldo Marzullo

Enrico Deusebio

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Table of Contents (15) Chapters

Preface

Section 1 – Introduction to Graph Machine Learning

Chapter 1: Getting Started with Graphs

Chapter 2: Graph Machine Learning

Section 2 – Machine Learning on Graphs

Chapter 3: Unsupervised Graph Learning

Chapter 4: Supervised Graph Learning

Chapter 5: Problems with Machine Learning on Graphs

Section 3 – Advanced Applications of Graph Machine Learning

Chapter 6: Social Network Graphs

Chapter 7: Text Analytics and Natural Language Processing Using Graphs

Chapter 8:Graph Analysis for Credit Card Transactions

Chapter 9: Building a Data-Driven Graph-Powered Application

Chapter 10: Novel Trends on Graphs

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The unsupervised graph embedding roadmap

Graphs are complex mathematical structures defined in a non-Euclidean space. Roughly speaking, this means that it is not always easy to define what is close to what; it might also be hard to say what close even means. Imagine a social network graph: two users can be respectively connected and yet share very different features—one might be interested in fashion and clothes, while the other might be interested in sports and videogames. Can we consider them as "close"?

For this reason, unsupervised machine learning algorithms have found large applications in graph analysis. Unsupervised machine learning is the class of machine learning algorithms that can be trained without the need for manually annotated data. Most of those models indeed make use of only information in the adjacency matrix and the node features, without any knowledge of the downstream machine learning task.

How is this possible? One of the most used solutions...

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Authors (3)

Claudio Stamile

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 Université 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.

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Aldo Marzullo

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 Université 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.

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Enrico Deusebio

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.

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