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Graph Learning Techniques

Graph Learning Techniques

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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.

Tjek vores konkurrenters priser
Normalpris
kr 516
Fragt: 39 kr
6 - 8 hverdage
20 kr
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God 4 anmeldelser på
Tjek vores konkurrenters priser

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.

Produktdetaljer
Sprog: Engelsk
Sider: 162
ISBN-13: 9781032851129
Indbinding: Paperback
Udgave:
ISBN-10: 1032851120
Udg. Dato: 26 feb 2025
Længde: 15mm
Bredde: 234mm
Højde: 156mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 26 feb 2025
Forfatter(e) Ren Ping Liu, Eryk Dutkiewicz, Baoling Shan, Xin Yuan, Wei Ni


Kategori Neurale net og fuzzy systemer


ISBN-13 9781032851129


Sprog Engelsk


Indbinding Paperback


Sider 162


Udgave


Længde 15mm


Bredde 234mm


Højde 156mm


Udg. Dato 26 feb 2025


Oplagsdato 26 feb 2025


Forlag Taylor & Francis Ltd

Kategori sammenhænge