Big Data of Complex Networks

ISBN-13: 9780367658434 / Angielski / Miękka / 2020 / 320 str.

Matthias Dehmer; Frank Emmert-Streib; Stefan Pickl

Opis

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

Spis treści

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.

O autorze

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.