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.
Original language | English |
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Pages (from-to) | 753-770 |
Number of pages | 18 |
Journal | Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences |
Volume | 466 |
Issue number | 2115 |
DOIs | |
Publication status | Published - 8 Mar 2010 |
Keywords
- birth and death process
- epidemiology
- network
- neuroscience
- random graph
- reproduction rate
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Commercial advantage through computational discovery of dynamic communicators in large digital networks
Desmond Higham (Participant), Ernesto Estrada (Participant) & Peter Grindrod (Participant)
Impact: Impact - for External Portal › Economic and commerce
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