Computational Network Theory
Theoretical Foundations and Applications
Quantitative and Network Biology

1. Edition October 2015
XXXIV, 242 Pages, Hardcover
93 Pictures (24 Colored Figures)
Handbook/Reference Book
Short Description
A comprehensive introduction to the topic as a branch of network theory, based on the understanding that computational networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data.
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This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data.
The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques.
With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks.
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Computing in Dynamic Networks
Visualization and Interactive Analysis for Complex Networks by means of Lossless Network Compression
Frank Emmert-Streib studied physics at the University of Siegen (Germany) gaining his PhD in theoretical physics from the University of Bremen (Germany). He received postdoctoral training from the Stowers Institute for Medical Re- search (Kansas City, USA) and the University of Washington (Seattle, USA). Currently, he is an associate professor at the Queen's University Belfast (UK) at the Center for Cancer Research and Cell Biology heading the Computational Biology and Machine Learning Laboratory. His main research interests are in the field of computational medicine, network biology and statistical genomics.