Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
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.
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.
Thể loại:Computers - Artificial Intelligence (AI)
Content Type:Sách
Năm:2025
Ngôn ngữ:english
Trang:162
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