Graph Learning Techniques by Baoling Shan Paperback Book

Publisher 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 ContentsAbstractList of FiguresList of TablesContributors1. Introduction 2. Privacy Considerations in Graph and Graph Learning3. Existing Technologies of Graph Learning4. Graph Extraction and Topology Learning of Band-limited Signals5. Graph Learning from Band-Limited Data by Graph Fourier Transform Analysis6. Graph Topology Learning of Brain Signals7. Graph Topology Learning of COVID-198. Preserving the Privacy of Latent Information for Graph-Structured Data9. Future Directions and Challenges10. AppendixBibliographyIndex

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