Search icon Close icon
Search icon
Subscription
0
Cart icon
Close icon
You have no products in your basket yet
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
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.
See other products by Enrico Deusebio
View More author details

Table of Contents (15) Chapters

Preface 1. Section 1 – Introduction to Graph Machine Learning
2. Chapter 1: Getting Started with Graphs 3. Chapter 2: Graph Machine Learning 4. Section 2 – Machine Learning on Graphs
5. Chapter 3: Unsupervised Graph Learning 6. Chapter 4: Supervised Graph Learning 7. Chapter 5: Problems with Machine Learning on Graphs 8. Section 3 – Advanced Applications of Graph Machine Learning
9. Chapter 6: Social Network Graphs 10. Chapter 7: Text Analytics and Natural Language Processing Using Graphs 11. Chapter 8:Graph Analysis for Credit Card Transactions 12. Chapter 9: Building a Data-Driven Graph-Powered Application 13. Chapter 10: Novel Trends on Graphs 14. Other Books You May Enjoy

Summary

In this section, we have provided you with the basic concepts of how to design, implement, and deploy data-driven applications that resort to graph modeling and leverage graph structures. We have highlighted the importance of a modular approach, which is usually the key to seamlessly scaling any data-driven use case from early-stage MVPs to production systems that can handle a large amount of data and large computational performances.

We have outlined the main architectural pattern, which should provide you with a guide when designing the backbone structure of your data-driven applications. We then continued by describing the main components that are the basis of graph-powered applications: graph processing engines, graph databases, and graph querying languages. For each component, we have provided an overview of the most common tools and libraries, with practical examples that will help you to build and implement your solutions. You should thus have by now a good overview...

lock icon The rest of the chapter is locked
arrow left Previous Chapter
Chapter 1 of 15
Next Chapter arrow right
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Sign up now
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Start free trial
Renews at €14.99/month. Cancel anytime}

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
Right arrow icon

Other recommended products

Related to this chapter
Left arrow icon
Network Science with Python and NetworkX Quick Start Guide
Network Science with Python and NetworkX Quick Start Guide
The emerging field of Network Science is about understanding different kind of relationships. This book covers the latest version 2.x of NetworkX for performing Network Science with Python.You will also learn the fundamentals of network theory and see practical examples of how they are applied to real-world problems using Python and NetworkX.
Apr 2019 6 hours 20 minutes
Hands-On Graph Analytics with Neo4j
Hands-On Graph Analytics with Neo4j
To start with you will cover the basics of graph analytics, Cypher querying language, components of graph architecture, and more. You will implement Neo4j techniques to understand various graph analytics methods to reveal complex relationships in data. You will understand how machine learning can be used to perform smarter graph analytics.
Aug 2020 17 hours 0 minutes
Geospatial Data Science Quick Start Guide
Geospatial Data Science Quick Start Guide
This book will help you leverage the power of data analysis and apply it to location and geospatial data to gain interesting insights. It presents useful tools and location intelligence techniques in Python to implement geospatial analytics from scratch.
May 2019 5 hours 40 minutes
Advanced Deep Learning with Python
Advanced Deep Learning with Python
This book is an expert-level guide to master the neural network variants using the Python ecosystem. You will gain the skills to build smarter, faster, and efficient deep learning systems with practical examples. By the end of this book, you will be up to date with the latest advances and current researches in the deep learning domain.
Dec 2019 15 hours 36 minutes
Mastering Machine Learning with Spark 2.x
Mastering Machine Learning with Spark 2.x
The purpose of machine learning is to build systems that learn from data. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.
Aug 2017 11 hours 20 minutes
Hands-On Mathematics for Deep Learning
Hands-On Mathematics for Deep Learning
The main aim of this book is to make the advanced mathematical background accessible to someone with a programming background. This book will equip the readers with not only deep learning architectures but the mathematics behind them. With this book, you will understand the relevant mathematics that goes behind building deep learning models.
Jun 2020 12 hours 8 minutes
Mastering Machine Learning Algorithms
Mastering Machine Learning Algorithms
A new second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems, updated to include Python 3.8 and TensorFlow 2.x as well as the latest in new algorithms and techniques.
Jan 2020 26 hours 36 minutes
Keras Reinforcement Learning Projects
Keras Reinforcement Learning Projects
Keras Reinforcement Learning Projects book teaches you essential concept, techniques and, models of reinforcement learning using best real-world demonstrations. You will explore popular algorithms such as Markov decision process, Monte Carlo, Q-learning making you equipped with complex statistics in various projects with the help of Keras
Sep 2018 9 hours 36 minutes
Mastering Java Machine Learning
Mastering Java Machine Learning
Master key Java machine learning libraries and their applications with the help of real-world case studies. Explore advanced machine learning techniques such as anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning.
Jul 2017 18 hours 32 minutes
40 Algorithms Every Programmer Should Know
40 Algorithms Every Programmer Should Know
Algorithms play an important role in both the science and practice of computing. To optimally use algorithms, a deeper understanding of their logic and mathematics is essential. Beyond traditional computing, the ability to apply these algorithms to solve real-world problems is a necessary skill, and this is what this book focuses on.
Jun 2020 12 hours 44 minutes
40 Algorithms Every Programmer Should Know
40 Algorithms Every Programmer Should Know
Algorithms play an important role in both the science and practice of computing. To optimally use algorithms, a deeper understanding of their logic and mathematics is essential. Beyond traditional computing, the ability to apply these algorithms to solve real-world problems is a necessary skill, and this is what this book focuses on.
Jun 2020 12 hours 44 minutes
Mastering Machine Learning Algorithms
Mastering Machine Learning Algorithms
This book is your guide to quickly get to grips with the most widely used machine learning algorithms. As a data science professional, this book will help you design and train better machine learning models to solve a variety of complex problems, and make the machine learn your requirements.
May 2018 19 hours 12 minutes
Right arrow icon

Personalised recommendations for you

Based on your interests and search pattern
Left arrow icon
Et al.
Et al.
Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.
Aug 2023 7 hours 40 minutes
Generative AI with LangChain
Generative AI with LangChain
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Dec 2023 12 hours 0 minutes
Generative AI with LangChain
Generative AI with LangChain
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Dec 2023 12 hours 0 minutes
Generative AI with LangChain
Generative AI with LangChain
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Dec 2023 12 hours 0 minutes
Generative AI with LangChain
Generative AI with LangChain
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Dec 2023 12 hours 0 minutes
Mastering Tableau 2023
Mastering Tableau 2023
This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.
Aug 2023 22 hours 48 minutes
Building AI Applications with ChatGPT APIs
Building AI Applications with ChatGPT APIs
This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.
Sep 2023 8 hours 36 minutes
Building AI Applications with ChatGPT APIs
Building AI Applications with ChatGPT APIs
This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.
Sep 2023 8 hours 36 minutes
Data Engineering with AWS
Data Engineering with AWS
Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.
Oct 2023 21 hours 12 minutes
Modern Data Architecture on AWS
Modern Data Architecture on AWS
Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.
Aug 2023 14 hours 0 minutes
Practical Guide to Applied Conformal Prediction in Python
Practical Guide to Applied Conformal Prediction in Python
Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.
Dec 2023 8 hours 0 minutes
TinyML Cookbook
TinyML Cookbook
With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.
Nov 2023 22 hours 8 minutes
Right arrow icon