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Evolving graphs : dynamical models, inverse problems and propagation

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. 753-770. ISSN 1364-5021

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    Abstract

    Applications such as neuroscience, telecommunication, on-line 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 range-dependent 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
    Related URLs:
    Depositing user: Mrs Irene Spencer
    Date Deposited: 13 Nov 2009 12:10
    Last modified: 03 Jul 2014 21:40
    URI: http://strathprints.strath.ac.uk/id/eprint/13407

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