Algorithmics Of Large And Complex Networks
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Algorithmics of Large and Complex Networks
Author: Jürgen Lerner
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-07-02
A state-of-the-art survey that reports on the progress made in selected areas of this important and growing field, aiding the analysis of existing networks and the design of new and more efficient algorithms for solving various problems on these networks.
Complex Computing-Networks
Author: Izzet Cem Göknar
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-05-31
This book contains the ceremonials and the proceedings pertaining to the Int- national Symposium CCN2005 on “Complex Computing-Networks: A Link between Brain-like and Wave-Oriented Electrodynamics Algorithms,” convened at Do ?u ? University of Istanbul, Turkey, on 13–14 June 2005, in connection with the bestowal of the honorary doctorate degrees on Professors Leopold B. Felsen and Leon O. Chua, for their extraordinary achievements in electromagnetics, and n- linear systems, respectively. The symposium was co-organized by Cem Göknar and Levent Sevgi, in consultation with Leopold B. Felsen and Leon O. Chua. Istanbul is a city with wonderful natural and historical surroundings, a city not only interconnecting Asia and Europe but also Eastern and Western cultures. Therefore, CCN2005 was a memorable event not only in the lifetime of Drs. Felsen, Chua, and their families, but also for all the other participants who were there to congratulate the recipients and participate in the symposium.
Big Data of Complex Networks
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 Universität der Bundeswehr München. 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 Universität München. 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.