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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
Authors (3):
Claudio Stamile Claudio Stamile
Profile icon Claudio Stamile
Aldo Marzullo Aldo Marzullo
Profile icon Aldo Marzullo
Enrico Deusebio Enrico Deusebio
Profile icon Enrico Deusebio
View More author details

Table of Contents (15) Chapters

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

Summary

In this chapter, we described how a classical fraud detection task can be described as a graph problem and how the techniques described in the previous chapter can be used to tackle the problem. Going into more detail, we introduced the dataset we used and described the procedure to transform the transactional data into two types of graph, namely, bipartite and tripartite undirected graphs. We then computed local (along with their distributions) and global metrics for both graphs, comparing the results.

Moreover, a community detection algorithm was applied to the graphs in order to spot and plot specific regions of the transaction graph where the density of fraudulent transactions is higher compared to the other communities.

Finally, we solved the fraud detection problem using supervised and unsupervised algorithms, comparing the performances of the bipartite and tripartite graphs. As the first step, since the problem was unbalanced with a higher presence of genuine transactions...

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