Graph Machine Learning
Packt Publishing Limited (Verlag)
978-1-80020-449-2 (ISBN)
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
Book DescriptionGraph 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.
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
Who this book is forThis 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.
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 Universite 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 Universite 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.
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
| Erscheinungsdatum | 30.06.2021 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-80020-449-3 / 1800204493 |
| ISBN-13 | 978-1-80020-449-2 / 9781800204492 |
| Zustand | Neuware |
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