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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

Product type Paperback

Published in Dec 2024

Publisher Packt

ISBN-13 9781835885963

Length 250 pages

Edition 1st Edition

Concepts

Data Governance

Authors (2):

Lakshya Khandelwal

Subhajoy Das

View More author details

Table of Contents (19) Chapters

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

Why subscribe?

18. Other Books You May Enjoy

Legal governance and compliance

GNNs can be useful tools in the legal and compliance sectors, offering innovative solutions to complex challenges. By leveraging graph structures to represent intricate relationships between legal entities, documents, and regulations, these techniques are revolutionizing how legal professionals and compliance officers approach their work.

Knowledge graph construction for legal and regulatory data

One of the primary applications of deep learning on graphs in the legal domain is the construction and utilization of knowledge graphs. These graphs serve as comprehensive repositories of legal and regulatory information, capturing complex relationships between various entities such as laws, regulations, court cases, and legal concepts:

Entity extraction and relation mapping: Deep learning models, particularly those based on NLP techniques, are employed to automatically extract entities and relationships from legal texts. GNNs can then be used...

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Authors (2)

Lakshya Khandelwal

Lakshya Khandelwal

Lakshya Khandelwal holds a bachelor's and master's degree from IIT Kanpur in mathematics and computer science and has 8+ years of experience in building scalable machine learning products for multiple tech giants. He has worked as a lead ML engineer with Samsung, building natural language intelligence for the very first version of Bixby. He has also worked as a data scientist with Adobe, developing search bid optimization solutions as part of the advertising cloud suite for major enterprises across the globe. In addition, he has led natural language and forecasting initiatives at Walmart, building next-generation AI products for millions of customers. Lakshya currently leads AI for AirMDR, building agentic AI for the cybersecurity domain.

See other products by Lakshya Khandelwal

Subhajoy Das

Subhajoy Das

Subhajoy Das is a staff data scientist with 7 years of experience under his belt. He graduated from IIT Kharagpur with a bachelor's and master's degree in mathematics and computing. Since then, he has worked in organizations at varying stages of growth: from fast-growing e-commerce start-ups such as Meesho to behemoths such as Adobe. He has driven several pivotal features in every company he has worked in, including building an end-to-end recommendation system for the Meesho app and curating interesting advertising using reinforcement learning-based optimizations in Adobe Advertising. He is currently working at Arista Networks, building AI-driven apps that are responsible for the cybersecurity of several Fortune 500 companies.

See other products by Subhajoy Das

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