Spatially embedded random networks.

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Spatially embedded random networks. Abstract Text:

L Barnett,E Di Paolo,S Bullock,L Barnett,E Di Paolo,S Bullock,

Many real-world networks analyzed in modern network theory have a natural spatial element; e.g., the Internet, social networks, neural networks, etc. Yet, aside from a comparatively small number of somewhat specialized and domain-specific studies, the spatial element is mostly ignored and, in particular, its relation to network structure disregarded. In this paper we introduce a model framework to analyze the mediation of network structure by spatial embedding; specifically, we model connectivity as dependent on the distance between network nodes. Our spatially embedded random networks construction is not primarily intended as an accurate model of any specific class of real-world networks, but rather to gain intuition for the effects of spatial embedding on network structure; nevertheless we are able to demonstrate, in a quite general setting, some constraints of spatial embedding on connectivity such as the effects of spatial symmetry, conditions for scale free degree distributions and the existence of small-world spatial networks. We also derive some standard structural statistics for spatially embedded networks and illustrate the application of our model framework with concrete examples.

Spatially embedded random networks. Publishing Authors By Initials

L Barnett,E Di Paolo,S Bullock,L Barnett,E Di Paolo,S Bullock,

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Spatially embedded random networks. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: Physical review. E, Statistical, nonlinear, and so

VOLUME: 76

Page Numbers: 056115

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ISSN: 1539-3755

DAY: 20

MONTH: 11

YEAR: 2007

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LANGUAGE: eng

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Grant and Affiliation Information for Spatially embedded random networks.

AFFILIATION: Centre for Computational Neuroscience and Robotics, Department of Informatics, School of Science and Technology, University of Sussex, Falmer, Brighton BN1 9QH, United Kingdom. lionelb@sussex.ac.uk

Country: United States

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MEDLINETA: Phys Rev E Stat Nonlin Soft Ma

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