Optimization and Scale-freeness for Complex Networks

Simple item page
dc.creatorMinnhagen, Petter
dc.creatorBernhardsson, Sebastian
dc.date2007-12-14
dc.date.accessioned2026-07-07T08:49:17Z
dc.date.available2026-07-07T08:49:17Z
dc.descriptionComplex networks are mapped to a model of boxes and balls where the balls are distinguishable. It is shown that the scale-free size distribution of boxes maximizes the information associated with the boxes provided configurations including boxes containing a finite fraction of the total amount of balls are excluded. It is conjectured that for a connected network with only links between different nodes, the nodes with a finite fraction of links are effectively suppressed. It is hence suggested that for such networks the scale-free node-size distribution maximizes the information encoded on the nodes. The noise associated with the size distributions is also obtained from a maximum entropy principle. Finally explicit predictions from our least bias approach are found to be born out by metabolic networks.
dc.description8 pages, 4 figures
dc.identifierhttps://arxiv.org/abs/0712.2349
dc.identifierhttp://arxiv.org/abs/0712.2349
dc.identifierChaos 17, 2 (2007)
dc.identifierdoi:10.1063/1.2720101
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/144247
dc.subjectStatistical Mechanics
dc.titleOptimization and Scale-freeness for Complex Networks
dc.typetext

Files

Collections

arXiv