Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms - Softcover

Claudio Stamile; Aldo Marzullo; Enrico Deusebio

 
9781800204492: Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms
Softcover
ISBN 10: 1800204493 ISBN 13: 9781800204492
Verlag: Packt Publishing, 2021

Inhaltsangabe

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 Description

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. You'll 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

Who this book is for

This book is for data analysts, graph developers, 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. The book will also be useful for data scientists and machine learning developers 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

  1. Getting Started with Graphs
  2. Graph Machine Learning
  3. Unsupervised Graph Learning
  4. Supervised Graph Learning
  5. Problems with Machine Learning on Graphs
  6. Social Network Graphs
  7. Text Analytics and Natural Language Processing Using Graphs
  8. Graph Analysis for Credit Card Transactions
  9. Building a Data-Driven Graph-Powered Application
  10. Novel Trends on Graphs

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Packt Publishing
Erscheinungsdatum
2021
Sprache
Englisch
ISBN 10 
1800204493
ISBN 13 
9781800204492
Einband
Taschenbuch
Anzahl der Seiten
338
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
gpsr@libri.de
gpsr@libri.de

Friedensallee 273
Hamburg
22763
Deutschland