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Full 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.
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
