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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

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

The dataset was created by collecting Facebook user information from survey participants. Ego networks were created from 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 we labeled 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 using the following URL...

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