Statistical and machine learning approaches for network analysis

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph...

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Bibliographic Details
Main Authors: Dehmer, Matthias, Basak, Subhash C.
Format: eBook Book
Language:English
Published: Hoboken, N.J Wiley 2012
John Wiley & Sons, Incorporated
Wiley-Blackwell
Edition:1
Series:Wiley Series in Computational Statistics
Subjects:
Communication
Communication - Network analysis - Graphic methods
Graphic methods
Information science
Information science > Statistical methods
Machine theory
Network analysis
Research > Statistical methods
Statistical methods
ISBN:9780470195154, 0470195150, 111834698X, 9781118346983, 1118346998, 9781118346990
Online Access:Get full text
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Summary:Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: * A survey of computational approaches to reconstruct and partition biological networks * An introduction to complex networks—measures, statistical properties, and models * Modeling for evolving biological networks * The structure of an evolving random bipartite graph * Density-based enumeration in structured data * Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.
Bibliography:HTTP:URL=http://catalogimages.wiley.com/images/db/jimages/9780470195154.jpg Information=Cover image
Includes bibliographical references and index
Available also in a print ed.
Mode of access: Internet via World Wide Web.
Title from title screen.
ISBN:9780470195154
0470195150
111834698X
9781118346983
1118346998
9781118346990
DOI:10.1002/9781118346990