Graph Learning Techniques by Shan Baoling (34 results)

Condition: New.

Condition: As New. Unread book in perfect condition.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: New. 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.…

Condition: New.

paperback. Condition: New.

Condition: New.

Condition: New. 2025. 1st Edition. paperback. . . . . .

Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.

Condition: As New. Unread book in perfect condition.

Condition: New. In English.

Condition: New. 2025. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Condition: NEW.

Paperback. Condition: Brand New. 200 pages. 9.19x6.13x9.21 inches. In Stock.

Condition: New. 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 Univers.

Condition: As New. Unread book in perfect condition.

Taschenbuch. Condition: Neu. Graph Learning Techniques | Baoling Shan (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | CRC Press | EAN 9781032851129 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu. …

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Hardback. Condition: New. New copy - Usually dispatched within 4 working days.

Paperback. Condition: New. 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.…

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Condition: New. In English.

Hardcover. Condition: Brand New. 200 pages. 9.19x6.13x9.21 inches. In Stock.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 186 pp. Englisch. …

Paperback. Condition: Brand New. 200 pages. 9.19x6.13x9.21 inches. In Stock. This item is printed on demand.