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Sách Graph Learning Techniques

120,000₫

Sách Graph Learning Techniques

Sách keo gáy, bìa mềm

Baoling Shan, Xin Yuan, Wei Ni, Ren Ping Liu, Eryk

Dutkiewicz. — CRC Press, 2025. — 162 p. — ISBN: 978-1-032-85112-9.

This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation.

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.

Thể loại:Computers - Artificial Intelligence (AI)

Content Type:Sách

Năm:2025

Ngôn ngữ:english

Trang:162