title: Spatially embedded random networks.
abstract: 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.
authors.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
authors.forename: L
authors.initials: L
authors.lastname: Barnett
authors.forename: E
authors.initials: E
authors.lastname: Di Paolo
authors.forename: S
authors.initials: S
authors.lastname: Bullock