Imperial College London
Alternate

Nick S Jones

Faculty of Natural Sciences, Department of Mathematics

Professor of Mathematical Sciences
 
 
 
//

Contact

 

+44 (0)20 7594 1146nick.jones

 
 
//

Location

 

301aSir Ernst Chain BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Garrod:2018:10.1103/PhysRevE.98.052316,
author = {Garrod, M and Jones, NS},
doi = {10.1103/PhysRevE.98.052316},
journal = {Physical Review E},
title = {Large algebraic connectivity fluctuations in spatial network ensembles imply a predictive advantage from node location information},
url = {http://dx.doi.org/10.1103/PhysRevE.98.052316},
volume = {98},
year = {2018}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A random geometric graph (RGG) ensemble is defined by the disordered distribution of its node locations. We investigate how this randomness drives sample-to-sample fluctuations in the dynamical properties of these graphs. We study the distributional properties of the algebraic connectivity which is informative of diffusion and synchronization time scales in graphs. We use numerical simulations to provide a characterization of the algebraic connectivity distribution for RGG ensembles. We find that the algebraic connectivity can show fluctuations relative to its mean on the order of 30%, even for relatively large RGG ensembles (N=105). We explore the factors driving these fluctuations for RGG ensembles with different choices of dimensionality, boundary conditions, and node distributions. Within a given ensemble, the algebraic connectivity can covary with the minimum degree and can also be affected by the presence of density inhomogeneities in the nodal distribution. We also derive a closed-form expression for the expected algebraic connectivity for RGGs with periodic boundary conditions for general dimension.
AU  - Garrod,M
AU  - Jones,NS
DO  - 10.1103/PhysRevE.98.052316
PY  - 2018///
SN  - 1539-3755
TI  - Large algebraic connectivity fluctuations in spatial network ensembles imply a predictive advantage from node location information
T2  - Physical Review E
UR  - http://dx.doi.org/10.1103/PhysRevE.98.052316
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000451579200007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/66012
VL  - 98
ER  -
Download