Graph Learning Techniques

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.

List 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

About the author

Baoling Shan is currently a Lecturer at University of Science and Technology Beijing, Beijing, China.
Xin Yuan
Wei Ni is a Principal Research Scientist at CSIRO, Sydney, Australia, a Fellow of IEEE, a Conjoint Professor at the University of New South Wales, an Adjunct Professor at the University of Technology Sydney, and an Honorary Professor at Macquarie University.
Ren Ping Liu is a Professor and the Head of the Discipline of Network and Cybersecurity, University of Technology Sydney (UTS), Ultimo, NSW, Australia.
Eryk Dutkiewicz is currently the Head of School of Electrical and Data Engineering at the University of Technology Sydney, Australia. He is a Senior Member of IEEE and his research interests cover 5G/6G and IoT networks.

Summary

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.

Product details

• Authors: Eryk Dutkiewicz, Ren Ping Liu, Wei Ni, Baoling Shan, Baoling Yuan Shan, Xin Yuan
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 20.02.2025
• EAN: 9781032851136
• ISBN: 978-1-0-3285113-6
• No. of pages: 162
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > Data communication, networks; Data Mining, MATHEMATICS / Graphic Methods, MATHEMATICS / Topology, geometry, Topology, Privacy & data protection, Privacy and data protection, Mathematical theory of computation, Applied mathematics, Legal aspects of IT, COMPUTERS / Data Science / Machine Learning, Digital and information technologies: Legal aspects, Neural networks and fuzzy systems, Neural networks & fuzzy systems