File(s) under permanent embargo
Spatially embedded random networks
journal contribution
posted on 2023-06-08, 11:39 authored by Lionel BarnettLionel Barnett, Ezequiel Di Paolo, S BullockMany real-world networks analysed 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 specialised 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 analyse 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 (SERN) 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.
History
Publication status
- Published
File Version
- Published version
Journal
Physical Review EISSN
1539-3755Publisher
American Physical SocietyExternal DOI
Issue
5Volume
76Page range
056115Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-05-21First Compliant Deposit (FCD) Date
2012-05-18Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC