Graph Learning Techniques. Este artículo no está disponible.
Idioma: inglés
Editorial: H N H International Limited, 2025
- Tapa blanda
- Nuevo

Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
Vendedor de AbeBooks desde 22 de noviembre de 2018
Condición: Nuevo
EUR 59,25
N° de ref. del artículo 26403527133
- Título
- Graph Learning Techniques
- Autor
- Shan, Baoling; Yuan, Xin; Ni, Wei; Liu, Ren Ping; Dutkiewicz, Eryk
- Editorial
- H N H International Limited
- Año de publicación
- 2025
- Estado
- New
- Encuadernación
- Encuadernación de tapa blanda
- Idioma
- inglés
- ISBN 10
- 1032851120
- ISBN 13
- 9781032851129
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.
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Acerca del autor
Baoling Shan is currently a Lecturer at University of Science and Technology Beijing, Beijing, China.
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.
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