Big Data of Complex Networks
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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.
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 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 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 HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.