Graph Learning Techniques

Regular price €64.99
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
A01=Baoling Shan
A01=Eryk Dutkiewicz
A01=Ren Ping Liu
A01=Wei Ni
A01=Xin Yuan
Anonymization
Author_Baoling Shan
Author_Eryk Dutkiewicz
Author_Ren Ping Liu
Author_Wei Ni
Author_Xin Yuan
brain connectivity modelling
Category=PBV
Category=UNF
Category=URD
Category=UYQN
Complexity-Analysis
data anonymisation methods
Differential-Privacy
Edge-Sparsity
epidemiological networks
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Ethical-Considerations
Graph Signals
Graph-Extraction
Graph-Fourier-Transform
Graph-Neural-Network
Graph-Process
Graph-Representation-Learning
Graph-Structured-Data
Graph-Topology
Graphical-Lasso
Laplacian-Eigenvalues-Estimation
network science
postgraduate resource
privacy techniques for scientific graphs
Privacy-Preserving
Pseudonymization
spectral analysis

Product details

  • ISBN 9781032851129
  • Weight: 300g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 Feb 2025
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
Secure checkout Fast Shipping Easy returns

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.

Baoling Shan is currently a Lecturer at University of Science and Technology Beijing, Beijing, China.

Xin Yuan is currently a Senior Research Scientist at CSIRO, Sydney, NSW, Australia, and an Adjunct Senior Lecturer at the University of New South Wales.

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.

More from this author