Big Data of Complex Networks

This book presents and explains the methods utilized to apply techniques from big data to massive structural data sets, especially those relating to the generation and analysis of very large networks or large sets of graphs. It is evident that the analysis of massive data representing complex networks may often involve statistical analyses such as sampling and bootstrapping. Novel techniques may be applied to the existing concepts in an interdisciplinary way. Special aspects such as computer memory are also investigated.

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 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.