Contagion on networks with self-organised community structure
Contagion on networks with self-organised community structure
Living systems are organised in space. This imposes constraints on both their structural form and, consequently, their dynamics. While artificial life research has demonstrated that embedding an adaptive system in space tends to have a significant impact on its behaviour, we do not yet have a full account of the relevance of spatiality to living self-organisation.
Here, we extend the REDS model of spatial networks with self-organised community structure to include the “small world” effect. We demonstrate that REDS networks can become small worlds with the introduction of a small amount of random rewiring. We then explore how this rewiring influences a simple dynamic process representing the contagious spread of infection or information.
We show that epidemic outbreaks arise more easily and spread faster on REDS networks compared to standard random geometric graphs (RGGs). Outbreaks spread even faster on randomly rewired small world REDS networks (due to their shorter path lengths) but initially find it more difficult to establish themselves (due to their reduced community structure). Overall, we find that small world REDS networks, with their combination of short characteristic path length, positive assortativity, strong community structure and high clustering, are more susceptible to a range of contagion dynamics than RGGs, and that they offer a useful abstract model for studying dynamics on spatially organised organic systems.
183-190
Antonioni, Alberto
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Bullock, Seth
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Darabos, Christian
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Giacobini, Mario
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Iotti, Bryan N.
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Moore, Jason H.
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Tomassini, Marco
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24 July 2015
Antonioni, Alberto
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Bullock, Seth
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Darabos, Christian
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Giacobini, Mario
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Iotti, Bryan N.
ca74329e-8ad7-47f3-ac7f-397e8ac08af1
Moore, Jason H.
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Tomassini, Marco
900e2019-588e-4a83-86d5-c5a108b462f3
Antonioni, Alberto, Bullock, Seth, Darabos, Christian, Giacobini, Mario, Iotti, Bryan N., Moore, Jason H. and Tomassini, Marco
(2015)
Contagion on networks with self-organised community structure.
Andrews, Paul, Caves, Leo, Doursat, Rene, Hickinbotham, Simon, Polack, Fiona, Stepney, Susan, Taylor, Tim and Timmis, Jon
(eds.)
In Advances in Artificial Life, ECAL 2015.
MIT Press.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Living systems are organised in space. This imposes constraints on both their structural form and, consequently, their dynamics. While artificial life research has demonstrated that embedding an adaptive system in space tends to have a significant impact on its behaviour, we do not yet have a full account of the relevance of spatiality to living self-organisation.
Here, we extend the REDS model of spatial networks with self-organised community structure to include the “small world” effect. We demonstrate that REDS networks can become small worlds with the introduction of a small amount of random rewiring. We then explore how this rewiring influences a simple dynamic process representing the contagious spread of infection or information.
We show that epidemic outbreaks arise more easily and spread faster on REDS networks compared to standard random geometric graphs (RGGs). Outbreaks spread even faster on randomly rewired small world REDS networks (due to their shorter path lengths) but initially find it more difficult to establish themselves (due to their reduced community structure). Overall, we find that small world REDS networks, with their combination of short characteristic path length, positive assortativity, strong community structure and high clustering, are more susceptible to a range of contagion dynamics than RGGs, and that they offer a useful abstract model for studying dynamics on spatially organised organic systems.
Text
ECAL15-Contagion.pdf
- Accepted Manuscript
More information
Accepted/In Press date: 28 April 2015
e-pub ahead of print date: 24 July 2015
Published date: 24 July 2015
Venue - Dates:
European Conference on Artificial Life (ECAL 2015), , York, United Kingdom, 2015-07-20 - 2017-07-24
Organisations:
Agents, Interactions & Complexity
Identifiers
Local EPrints ID: 376659
URI: http://eprints.soton.ac.uk/id/eprint/376659
PURE UUID: cfae83d0-0fc2-4032-8d47-e7edb7dff3c4
Catalogue record
Date deposited: 07 May 2015 07:38
Last modified: 14 Mar 2024 19:48
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Contributors
Author:
Alberto Antonioni
Author:
Christian Darabos
Author:
Mario Giacobini
Author:
Bryan N. Iotti
Author:
Jason H. Moore
Author:
Marco Tomassini
Editor:
Paul Andrews
Editor:
Leo Caves
Editor:
Rene Doursat
Editor:
Simon Hickinbotham
Editor:
Fiona Polack
Editor:
Susan Stepney
Editor:
Tim Taylor
Editor:
Jon Timmis
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