You're reading from Graph Machine Learning

Product typeBook

Published inJun 2021

PublisherPackt

ISBN-139781800204492

Edition1st Edition

Concepts

Machine Learning

Authors (3):

Claudio Stamile

Aldo Marzullo

Enrico Deusebio

View More author details

What this book covers

Chapter 1, Getting Started with Graphs, introduces the basic concepts of graph theory using the NetworkX Python library.

Chapter 2, Graph Machine Learning, introduces the main concepts of graph machine learning and graph embedding techniques.

Chapter 3, Unsupervised Graph Learning, covers recent unsupervised graph embedding methods.

Chapter 4, Supervised Graph Learning, covers recent supervised graph embedding methods.

Chapter 5, Problems with Machine Learning on Graphs, introduces the most common machine learning tasks on graphs.

Chapter 6, Social Network Analysis, shows an application of machine learning algorithms on social network data.

Chapter 7, Text Analytics and Natural Language Processing Using Graphs, shows the application of machine learning algorithms to natural language processing tasks.

Chapter 8, Graph Analysis for Credit Card Transactions, shows the application of machine learning algorithms to credit card fraud detection.

Chapter 9, Building a Data-Driven Graph-Powered Application, introduces some technologies and techniques that are useful for dealing with large graphs.

Chapter 10, Novel Trends on Graphs, introduces some novel trends (algorithms and applications) in graph machine learning.

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Graph Machine Learning

Published in: Jun 2021Publisher: PacktISBN-13: 9781800204492

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.
Read more about Claudio Stamile

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.
Read more about Aldo Marzullo

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.
Read more about Enrico Deusebio

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