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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
Authors (3):
Claudio Stamile Claudio Stamile
Profile icon Claudio Stamile
LinkedIn
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.
See other products by Claudio Stamile
Aldo Marzullo Aldo Marzullo
Profile icon Aldo Marzullo
LinkedIn
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.
See other products by Aldo Marzullo
Enrico Deusebio Enrico Deusebio
Profile icon Enrico Deusebio
LinkedIn
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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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 Other Books You May Enjoy

Overview of Lambda architectures

In recent years, great focus has been given to designing scalable architectures that will allow, on the one hand, the processing of a large amount of data, and, on the other, providing answers/alerts/actions in real time, using the latest available information.

Besides, these systems need to also be able to scale out seamlessly to a larger number of users or a larger amount of data by increasing resources horizontally (adding more servers) or vertically (using servers that are more powerful). Lambda architecture is a particular data-processing architecture that is designed to process massive quantities of data and ensure large throughput in a very efficient manner, preserving reduced latency and ensuring fault tolerance and negligible errors.

The Lambda architecture is composed of three different layers:

  • The batch layer: This layer sits on top of the (possibly distributed and scalable) storage system, and can handle and store all historical...
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Authors (3)

Left arrow icon
Profile icon Claudio Stamile
LinkedIn
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
See other products by Claudio Stamile
Profile icon Aldo Marzullo
LinkedIn
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
See other products by Aldo Marzullo
Profile icon Enrico Deusebio
LinkedIn
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
See other products by Enrico Deusebio
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