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- DescriptionBig 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 vel 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 invative techniques to help readers handle big data Matthias Dehmer received his PhD in computer science from the Darmstadt University of Techlogy, 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 Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Techlogy, 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 Techlogy, 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 Techlogy. 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 Techlogy.
- Author BiographyMatthias 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 Universitat der Bundeswehr Munchen. 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 Emmert-Streib 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 high-throughput data from genomics and genetics experiments.
- PublisherTaylor & Francis Inc
- Date of Publication24/08/2016
- SubjectComputing: Professional & Programming
- Series TitleChapman & Hall/CRC Big Data Series
- Place of PublicationPortland
- Country of PublicationUnited States
- ImprintProductivity Press
- Content Note110 black & white illustrations, 23 black & white tables
- Weight771 g
- Width178 mm
- Height254 mm
- Spine23 mm
- Edited byAndreas Holzinger,Frank Emmert-Streib,Matthias Dehmer,Stefan Pickl
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