Graph Learning Techniques
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 26 February 2025
- ISBN 9781032851136
- Binding Hardback
- No. of pages180 pages
- Size 234x156 mm
- Weight 453 g
- Language English
- Illustrations 122 Illustrations, black & white; 49 Halftones, black & white; 73 Line drawings, black & white; 11 Tables, black & white 641
Categories
Short description:
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. A valuable reference for advance undergraduate and postgraduate students in Network Analysis, Privacy and Security in Data Analytics, Graph Theory, and Applications in Healthcare.
MoreLong description:
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation.
It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning.
This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.
Table of Contents:
Table of Contents
Abstract
List of Figures
List of Tables
Contributors
1. Introduction
2. Privacy Considerations in Graph and Graph Learning
3. Existing Technologies of Graph Learning
4. Graph Extraction and Topology Learning of Band-limited Signals
5. Graph Learning from Band-Limited Data by Graph Fourier Transform Analysis
6. Graph Topology Learning of Brain Signals
7. Graph Topology Learning of COVID-19
8. Preserving the Privacy of Latent Information for Graph-Structured Data
9. Future Directions and Challenges
10. Appendix
Bibliography
Index
More
