title: Optimization and scale-freeness for complex networks.
abstract: 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.
authors.affiliation: Department of Physics, Umeå University, 901 87 Umeå, Sweden.
authors.forename: Petter
authors.initials: P
authors.lastname: Minnhagen
authors.forename: Sebastian
authors.initials: S
authors.lastname: Bernhardsson