Grindrod, Peter and Higham, D.J. (2010) Evolving graphs : dynamical models, inverse problems and propagation. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 466 (2115). pp. 753770. ISSN 13645021

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Abstract
Applications such as neuroscience, telecommunication, online social networking, transport and retail trading give rise to connectivity patterns that change over time. In this work we address the resulting need for network models and computational algorithms that deal with dynamic links. We introduce a new class of evolving rangedependent random graphs that gives a realistic but tractable framework for modeling and simulation. We develop a spectral algorithm for calibrating a set of edge ranges from a sequence of network snapshots, and give a proof of principle illustration on some neuroscience data. We also show how the model can be used computationally and analytically to investigate the scenario where an evolutionary process, such as an epidemic, takes place on an evolving network. This allows us to study the cumulative effect of two distinct types of dynamics.
Item type:  Article 

ID code:  13407 
Keywords:  birth and death process, epidemiology, network, neuroscience, random graph, reproduction rate, Probabilities. Mathematical statistics, Mathematics 
Subjects:  Science > Mathematics > Probabilities. Mathematical statistics Science > Mathematics 
Department:  Faculty of Science > Mathematics and Statistics 
Depositing user:  Mrs Irene Spencer 
Date Deposited:  13 Nov 2009 12:10 
Last modified:  26 Mar 2015 19:01 
URI:  http://strathprints.strath.ac.uk/id/eprint/13407 
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