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Applied Deep Learning on Graphs

You're reading from   Applied Deep Learning on Graphs Leverage graph data for business applications using specialized deep learning architectures

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Product type Paperback
Published in Dec 2024
Publisher Packt
ISBN-13 9781835885963
Length 250 pages
Edition 1st Edition
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Authors (2):
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Lakshya Khandelwal Lakshya Khandelwal
Author Profile Icon Lakshya Khandelwal
Lakshya Khandelwal
Subhajoy Das Subhajoy Das
Author Profile Icon Subhajoy Das
Subhajoy Das
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1: Foundations of Graph Learning
2. Chapter 1: Introduction to Graph Learning FREE CHAPTER 3. Chapter 2: Graph Learning in the Real World 4. Chapter 3: Graph Representation Learning 5. Part 2: Advanced Graph Learning Techniques
6. Chapter 4: Deep Learning Models for Graphs 7. Chapter 5: Graph Deep Learning Challenges 8. Chapter 6: Harnessing Large Language Models for Graph Learning 9. Part 3: Practical Applications and Implementation
10. Chapter 7: Graph Deep Learning in Practice 11. Chapter 8: Graph Deep Learning for Natural Language Processing 12. Chapter 9: Building Recommendation Systems Using Graph Deep Learning 13. Chapter 10: Graph Deep Learning for Computer Vision 14. Part 4: Future Directions
15. Chapter 11: Emerging Applications 16. Chapter 12: The Future of Graph Learning 17. Index 18. Other Books You May Enjoy

Introduction to Graph Learning

Graph data is a powerful and intuitive way of expressing information, and several practical scenarios can be better expressed using graph data than tabular approaches. Analyzing graph data has been a topic of study for decades, but it has only recently begun to capture the limelight due to advances in compute capabilities.

In this book, we aim to introduce you to the world of graphs. Here, we’ll begin by discussing what graph data is and the fundamental mathematical terminologies surrounding graphs. Next, we’ll take a small detour and discuss some common graph algorithms and their applications in graph data analytics. We’ll extend our discussion on graph data analytics to the requirement of graph deep learning and why it stands as a specialized subdomain compared to applying existing architectures.

In this chapter, we’ll cover the following topics:

  • Do we need graphs?
  • Formalizing graphs
  • Types and properties...
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Applied Deep Learning on Graphs
Published in: Dec 2024
Publisher: Packt
ISBN-13: 9781835885963
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