Optimization and scale-freeness for complex networks
2007 (English)In: Chaos, ISSN 1054-1500, Vol. 17, no 026117, 7- p.Article in journal (Refereed) Published
Complex 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 borne out by metabolic networks.
Place, publisher, year, edition, pages
2007. Vol. 17, no 026117, 7- p.
Statistical physics, Networks
Condensed Matter Physics
IdentifiersURN: urn:nbn:se:umu:diva-6521DOI: 10.1063/1.2720101OAI: oai:DiVA.org:umu-6521DiVA: diva2:146190