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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

Overview of the dataset

The dataset used in this chapter is the Credit Card Transactions Fraud Detection Dataset available on Kaggle at the following URL: https://www.kaggle.com/kartik2112/fraud-detection?select=fraudTrain.csv.

The dataset is made up of simulated credit card transactions containing legitimate and fraudulent transactions for the period January 1, 2019 – December 31, 2020. It includes the credit cards of 1,000 customers performing transactions with a pool of 800 merchants. The dataset was generated using Sparkov Data Generation. More information about the generation algorithm is available at the following URL: https://github.com/namebrandon/Sparkov_Data_Generation.

For each transaction, the dataset contains 23 different features. In the following table, we will show only the information that will be used in this chapter:

Table 8.1 – List of variables used in the dataset

For the purposes of our analysis, we will use the fraudTrain...

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