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Graph Machine Learning

You're reading from   Graph Machine Learning Learn about the latest advancements in graph data to build robust machine learning models

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Product type Paperback
Published in Jul 2025
Publisher Packt
ISBN-13 9781803248066
Length 434 pages
Edition 2nd Edition
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Authors (3):
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Aldo Marzullo Aldo Marzullo
Author Profile Icon Aldo Marzullo
Aldo Marzullo
Enrico Deusebio Enrico Deusebio
Author Profile Icon Enrico Deusebio
Enrico Deusebio
Claudio Stamile Claudio Stamile
Author Profile Icon Claudio Stamile
Claudio Stamile
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Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1: Introduction to Graph Machine Learning
2. Getting Started with Graphs FREE CHAPTER 3. Graph Machine Learning 4. Neural Networks and Graphs 5. Part 2: Machine Learning on Graphs
6. Unsupervised Graph Learning 7. Supervised Graph Learning 8. Solving Common Graph-Based Machine Learning Problems 9. Part 3: Practical Applications of Graph Machine Learning
10. Social Network Graphs 11. Text Analytics and Natural Language Processing Using Graphs 12. Graph Analysis for Credit Card Transactions 13. Building a Data-Driven Graph-Powered Application 14. Part 4: Advanced topics in Graph Machine Learning
15. Temporal Graph Machine Learning 16. GraphML and LLMs 17. Novel Trends on Graphs 18. Index
19. Other Books You May Enjoy

Predicting missing links in a graph

Link prediction, also known as graph completion, is a common problem when dealing with graphs. More precisely, from a partially observed graph—that is a graph for which only a portion of the existing edges are known—we want to predict whether or not an edge exists between any given node pairs, as seen in Figure 6.1. Formally, let be a graph where is its set of nodes and is its set of edges. The set of edges are known as observed links, while the set of edges are known as unknown links. The goal of the link prediction problem is to exploit the information of and to estimate . The partially observed graph can be seen here:

Figure 6.1: Partially observed graph with observed link  (solid lines) and unknown link  (dashed lines)

Figure 6.1: Partially observed graph with observed link (solid lines) and unknown link (dashed lines)

The link prediction problem is widely used in different domains, such as a recommender system, in order to propose friendships in social networks or items to purchase on e-commerce websites. It...

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