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• W2106180925 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." @default.
• W2106180925 created "2016-06-24" @default.
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• W2106180925 date "2007-11-20" @default.
• W2106180925 modified "2023-10-18" @default.
• W2106180925 title "Spatially embedded random networks" @default.
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• W2106180925 doi "https://doi.org/10.1103/physreve.76.056115" @default.
• W2106180925 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18233726" @default.
• W2106180925 hasPublicationYear "2007" @default.
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