BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Graph Learning Techniques

Graph Learning Techniques

Graph Learning Techniques
Stock photo: cover may vary

Graph Learning Techniques Paperback - 2025

by Baoling Shan

Add to wish list
  • Used
New

Description

like new.
Ask the seller a question Add to wish list
A$136.02
A$5.77 Delivery within USA
Standard delivery: 2 to 14 days
More delivery options
Ships from GreatBookPrices (Maryland, United States)
The BIBLIO Guarantee

Details

  • Title Graph Learning Techniques
  • Author Baoling Shan
  • Binding Paperback
  • Condition New
  • Pages 162
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date 2025-02-26
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # 48257425
  • ISBN 9781032851129 / 1032851120
  • Weight 0.59 lbs (0.27 kg)
  • Dimensions 9.21 x 6.14 x 0.4 in (23.39 x 15.60 x 1.02 cm)
  • Category Computers - General Information
  • Library of Congress subjects Signal processing, Graph theory - Data processing
  • Library of Congress Catalogue Number 2024041867
  • Quantity available 1

About GreatBookPrices Maryland, United States

Biblio member since 2024

Since 1991, we have worked every day to serve our customers with state-of-the-art technology and world class service. We are dedicated to providing customers around the world with the widest selection of books, DVDs, and CDs at the absolute lowest price.

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from GreatBookPrices

Reader reviews for Graph Learning Techniques

Write a review for this book

There are no reviews for this book yet. Be the first to write a review!

Your review will be anonymous unless you login or create an account first.

Overall rating
Remember: You’re rating the book as a work, not the seller or the condition of the specific copy you purchased!

Important Terms and Guidelines

  • Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
  • Reviews must be at least 140 characters in length.
  • Please do not reveal critical plot elements.
  • This is not a help line. Contact customer support if you need help.
Your review must not include:
  • Obscenities, discriminatory language, or other insulting language not suitable for public domain
  • Advertisements, “spam” content, or references to other products, offers or websites.
  • Email addresses, URLs, phone numbers, physical addresses or other contact information.
  • Overly critical comments about other reviews or reviewers
  • Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
  • Availability, price, or alternative ordering/shipping information

From the publisher

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.

About the author

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 University of New South Wales.

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.

Please wait a moment while we load the requested page.

tracking-