Coverart for item
The Resource Big data of complex networks, edited by Matthias Dehmer, Frank Emmert-Streib, Stefan Pickl, Andreas Holzinger, (electronic book)

Big data of complex networks, edited by Matthias Dehmer, Frank Emmert-Streib, Stefan Pickl, Andreas Holzinger, (electronic book)

Label
Big data of complex networks
Title
Big data of complex networks
Statement of responsibility
edited by Matthias Dehmer, Frank Emmert-Streib, Stefan Pickl, Andreas Holzinger
Contributor
Editor
Subject
Language
eng
Summary
Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks. Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics. Key features: Provides a complete discussion of both the hardware and software used to organize big data Describes a wide range of useful applications for managing big data and resultant data sets Maintains a firm focus on massive data and large networks Unveils innovative techniques to help readers handle big data Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT - The Health and Life Sciences University, Austria, and the Universitt der Bundeswehr Mnchen. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory. Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine. Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universitt Mnchen. His research interests are in operations research, systems biology, graph theory and discrete optimization. Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology
Member of
Cataloging source
  • StDuBDS
  • StDuBDS
Dewey number
005.7
Illustrations
illustrations
Index
index present
LC call number
QA402
LC item number
.B485 2017
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorDate
  • 1968-
  • 1967-
http://library.link/vocab/relatedWorkOrContributorName
  • Dehmer, Matthias
  • Emmert-Streib, Frank
  • Pickl, Stefan
  • Holzinger, Andreas
Series statement
Chapman & Hall/CRC big data series
http://library.link/vocab/subjectName
  • Big data
  • Large scale systems
  • Computer networks
  • Computational complexity
Target audience
specialized
Label
Big data of complex networks, edited by Matthias Dehmer, Frank Emmert-Streib, Stefan Pickl, Andreas Holzinger, (electronic book)
Instantiates
Publication
Note
"A Chapman & Hall book."
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier MARC source
rdacarrier
Content category
  • text
  • still image
Content type MARC source
  • rdacontent
  • rdacontent
Contents
<p>Big Data of Complex Networks: Challenges and Perspectives. Theory and Practice of Sampling Large Networks. Scale Graph: Large-Scale Graph Analytics Library. Techniques for the Management and Querying of Big Data in Large Scale Communication Networks. Fast Heuristics for Some Covering and Dominating Problems in Large-Scale Graphs. Aspects of Large Network in Economy. Network Visualization in the Context of Large Network Analysis.</p>
Control code
AH29980421
Extent
xii, 319 pages
Form of item
electronic
Governing access note
After 5 minutes Preview, click on &#x32;Request Access&#x33;, fill in a form with your details. If triggered, the book will be loaned and tied to the one user for 1 week, during which time users can read or download as they choose. 4th user request triggers auto-purchase
Isbn
9781498723619
Lccn
2016003196
Media category
computer
Media MARC source
rdamedia
Other physical details
illustrations (black and white)
Specific material designation
remote
Label
Big data of complex networks, edited by Matthias Dehmer, Frank Emmert-Streib, Stefan Pickl, Andreas Holzinger, (electronic book)
Publication
Note
"A Chapman & Hall book."
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier MARC source
rdacarrier
Content category
  • text
  • still image
Content type MARC source
  • rdacontent
  • rdacontent
Contents
<p>Big Data of Complex Networks: Challenges and Perspectives. Theory and Practice of Sampling Large Networks. Scale Graph: Large-Scale Graph Analytics Library. Techniques for the Management and Querying of Big Data in Large Scale Communication Networks. Fast Heuristics for Some Covering and Dominating Problems in Large-Scale Graphs. Aspects of Large Network in Economy. Network Visualization in the Context of Large Network Analysis.</p>
Control code
AH29980421
Extent
xii, 319 pages
Form of item
electronic
Governing access note
After 5 minutes Preview, click on &#x32;Request Access&#x33;, fill in a form with your details. If triggered, the book will be loaned and tied to the one user for 1 week, during which time users can read or download as they choose. 4th user request triggers auto-purchase
Isbn
9781498723619
Lccn
2016003196
Media category
computer
Media MARC source
rdamedia
Other physical details
illustrations (black and white)
Specific material designation
remote

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