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Spatial embedding and the structure of complex networks

Spatial embedding and the structure of complex networks
Spatial embedding and the structure of complex networks
We review and discuss the structural consequences of embedding a random network within a metric space such that nodes distributed in this space tend to be connected to those nearby. We find that where the spatial distribution of nodes is maximally symmetrical some of the structural properties of the resulting networks are similar to those of random non-spatial networks. However, where the distribution of nodes is inhomogeneous in some way, this ceases to be the case, with consequences for the distribution of neighbourhood sizes within the network, the correlation between the number of neighbours of connected nodes, and the way in which the largest connected component of the network grows as the density of edges is increased. We present an overview of these findings in an attempt to convey the ramifications of spatial embedding to those studying real-world complex systems.
20-28
Bullock, Seth
2ad576e4-56b8-4f31-84e0-51bd0b7a1cd3
Barnett, Lionel
df5b0411-ee06-4f89-b8c8-a120d8644aef
Di Paolo, Ezequiel A.
d2b9b2ea-b23c-4350-b385-f0d0d8718755
Bullock, Seth
2ad576e4-56b8-4f31-84e0-51bd0b7a1cd3
Barnett, Lionel
df5b0411-ee06-4f89-b8c8-a120d8644aef
Di Paolo, Ezequiel A.
d2b9b2ea-b23c-4350-b385-f0d0d8718755

Bullock, Seth, Barnett, Lionel and Di Paolo, Ezequiel A. (2010) Spatial embedding and the structure of complex networks. Complexity, 16 (2), 20-28.

Record type: Article

Abstract

We review and discuss the structural consequences of embedding a random network within a metric space such that nodes distributed in this space tend to be connected to those nearby. We find that where the spatial distribution of nodes is maximally symmetrical some of the structural properties of the resulting networks are similar to those of random non-spatial networks. However, where the distribution of nodes is inhomogeneous in some way, this ceases to be the case, with consequences for the distribution of neighbourhood sizes within the network, the correlation between the number of neighbours of connected nodes, and the way in which the largest connected component of the network grows as the density of edges is increased. We present an overview of these findings in an attempt to convey the ramifications of spatial embedding to those studying real-world complex systems.

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Bullock-Barnett-DiPaolo-Complexity.pdf - Accepted Manuscript
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Published date: 2010
Organisations: Agents, Interactions & Complexity

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Local EPrints ID: 271063
URI: http://eprints.soton.ac.uk/id/eprint/271063
PURE UUID: 8afe532a-8f02-4e8a-9c18-b7fddc6ce1e7

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Date deposited: 13 May 2010 08:19
Last modified: 19 Jul 2019 22:14

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