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Applied Deep Learning on Graphs

You're reading from   Applied Deep Learning on Graphs Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures

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Product type Paperback
Published in Dec 2024
Publisher Packt
ISBN-13 9781835885963
Length
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 FREE CHAPTER
2. Chapter 1: Introduction to Graph Learning 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

Traditional graph-based solutions

Many computer scientists have etched their names in history by devising elegant solutions to seemingly complex problems involving graphs. However, graphs aren’t just confined to the algorithm books, and graph-based problems are common in the wild. Lots of business problems and scientific research can be boiled down to graph-based problems, on which existing solutions can be implemented to generate the required output. In this section, we’ll talk about the most popular problems in the domain of graphs, a few approaches to solving them, and where these problems are encountered in practical scenarios.

Searching

There are two fundamental approaches when performing a search over a graph: breadth-first and depth-first. Both are means to traverse a graph from a starting point to all nodes that can be reached from the initial node, but the differentiating factor is their approach.

In BFS, the algorithm explores a graph level by level...

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