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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

Overview of the dataset

We will be using a SNAP public dataset, social circles: Facebook, from Stanford University (https://snap.stanford.edu/data/ego-Facebook.html).

The dataset was created by collecting Facebook user information from survey participants. In more detail, ego networks were created for 10 users. Each user was asked to identify all the circles (list of friends) to which their friends belong. On average, each user identified 19 circles in their ego networks, where each circle has on average 22 friends.

For each user, the following information was collected:

  • Edges: An edge exists if two users are friends on Facebook.
  • Node features: Features are scored as 1 if the user has this property in their profile and 0 otherwise. Features have been anonymized since the names of the features would reveal private data.

The 10 ego networks were then unified in a single graph that we are going to study.

Dataset download

The dataset can be retrieved...

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