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Graph Machine Learning

You're reading from   Graph Machine Learning Take graph data to the next level by applying machine learning techniques and algorithms

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Product type Paperback
Published in Jun 2021
Publisher Packt
ISBN-13 9781800204492
Length 338 pages
Edition 1st Edition
Languages
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Authors (3):
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Aldo Marzullo Aldo Marzullo
Author Profile Icon Aldo Marzullo
Aldo Marzullo
Claudio Stamile Claudio Stamile
Author Profile Icon Claudio Stamile
Claudio Stamile
Enrico Deusebio Enrico Deusebio
Author Profile Icon Enrico Deusebio
Enrico Deusebio
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1 – Introduction to Graph Machine Learning
2. Chapter 1: Getting Started with Graphs FREE CHAPTER 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

Chapter 9: Building a Data-Driven Graph-Powered Application

So far, we have provided you with both theoretical and practical ideas to allow you to design and implement machine learning models that leverage graph structures. Besides designing the algorithm, it is often very important to embed the modeling/analytical pipeline into a robust and reliable end-to-end application. This is especially true in industrial applications, where the end goal is usually to design and implement production systems that support data-driven decisions and/or provide users with timely information. However, creating a data-driven application that resorts to graph representation/modeling is indeed a challenging task that requires a proper design that is a lot more complicated than simply importing networkx. This chapter aims to provide you with a general overview of the key concepts and frameworks that are used when building graph-based, scalable, data-driven applications.

We will start by providing an...

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