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Statistical and machine learning approaches for network analysis [electronic resource] / edited by Matthias Dehmer, Subhash C. Basak.

Contributor(s): Material type: TextTextPublication details: Hoboken, N.J. : Wiley, 2012.Description: xii, 331 p. : illSubject(s): Genre/Form: DDC classification:
  • 511/.5 23
LOC classification:
  • Q180.55.S7 S73 2012
Online resources: Summary: "This book explores novel graph classes and presents novel methods to classify networks. It particularly addresses the following problems: exploration of novel graph classes and their relationships among each other; existing and classical methods to analyze networks; novel graph similarity and graph classification techniques based on machine learning methods; and applications of graph classification and graph mining. Key topics are addressed in depth including the mathematical definition of novel graph classes, i.e. generalized trees and directed universal hierarchical graphs, and the application areas in which to apply graph classes to practical problems in computational biology, computer science, mathematics, mathematical psychology, etc"-- Provided by publisher.
Holdings
Item type Current library Call number Status Date due Barcode
Ebook TUS: Midlands, Main Library Athlone Online eBook (Browse shelf(Opens below)) Available

Includes bibliographical references and index.

"This book explores novel graph classes and presents novel methods to classify networks. It particularly addresses the following problems: exploration of novel graph classes and their relationships among each other; existing and classical methods to analyze networks; novel graph similarity and graph classification techniques based on machine learning methods; and applications of graph classification and graph mining. Key topics are addressed in depth including the mathematical definition of novel graph classes, i.e. generalized trees and directed universal hierarchical graphs, and the application areas in which to apply graph classes to practical problems in computational biology, computer science, mathematics, mathematical psychology, etc"-- Provided by publisher.

Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.

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