Big Data of Complex Networks
Matthias Dehmer (Redaktør) ; Frank EmmertStreib (Redaktør) ; Stefan Pickl (Redaktør) ; Andreas Holzinger (Redaktør)
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. Les mer
 Vår pris
 2363,
(Innbundet)
Fri frakt!
Leveringstid:
Sendes innen 21 dager
Innbundet
Legg i
Innbundet
Legg i
Vår pris:
2363,
(Innbundet)
Fri frakt!
Leveringstid:
Sendes innen 21 dager
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 Universitat der Bundeswehr Munchen. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory.
Frank EmmertStreib 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 Universitat Munchen. 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 HCIKDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.
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 Universitat der Bundeswehr Munchen. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory.
Frank EmmertStreib 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 Universitat Munchen. 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 HCIKDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.
 FAKTA

Utgitt:
2016
Forlag: Productivity Press
Innbinding: Innbundet
Språk: Engelsk
Sider: 320
ISBN: 9781498723619
Format: 25 x 18 cm
 KATEGORIER:
 VURDERING

Gi vurdering
Les vurderinger
Big Data of Complex Networks: Challenges and Perspectives. Theory and Practice of Sampling Large Networks. Scale Graph: LargeScale
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 LargeScale Graphs. Aspects of Large Network in Economy. Network Visualization
in the Context of Large Network Analysis.
Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his PhD in computer science from the
Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna BioCenter (Austria), Vienna University
of Technology, and University of Coimbra (Coimbra). He obtained his habilitation in applied discrete mathematics from the
Vienna University of Technology. Currently, he is Professor at UMIT – The Health and Life Sciences University (Austria) and
also holds a position at the Universit¨at der Bundeswehr M¨unchen. His research interests are in applied mathematics, bioinformatics,
systems biology, graph theory, complexity, and information theory. He has written over 175 publications in his research areas.
Frank EmmertStreib studied physics at the University of Siegen, Germany, gaining his PhD in theoretical physics from the University of Bremen. He was a postdoctoral research associate at the Stowers Institute for Medical Research, Kansas City, USA, and a senior fellow at the University of Washington, Seattle, USA. Currently, he is a lecturer/assistant professor at the Queen’s University Belfast, UK, at the Center for Cancer Research and Cell Biology, heading the Computational Biology and Machine Learning Lab. His research interests are in the field of computational biology, machine learning, and biostatistics in the development and application of methods from statistics and machine learning for the analysis of highthroughput data from genomics and genetics experiments.
Frank EmmertStreib studied physics at the University of Siegen, Germany, gaining his PhD in theoretical physics from the University of Bremen. He was a postdoctoral research associate at the Stowers Institute for Medical Research, Kansas City, USA, and a senior fellow at the University of Washington, Seattle, USA. Currently, he is a lecturer/assistant professor at the Queen’s University Belfast, UK, at the Center for Cancer Research and Cell Biology, heading the Computational Biology and Machine Learning Lab. His research interests are in the field of computational biology, machine learning, and biostatistics in the development and application of methods from statistics and machine learning for the analysis of highthroughput data from genomics and genetics experiments.