English

Uncorrelated Random Networks

Statistical Mechanics 2009-11-07 v3 Disordered Systems and Neural Networks

Abstract

We define a statistical ensemble of non-degenerate graphs, i.e. graphs without multiple- and self-connections between nodes. The node degree distribution is arbitrary, but the nodes are assumed to be uncorrelated. This completes our earlier publication \cite{bck}, where trees and degenerate graphs were considered. An efficient algorithm generating non-degenerate graphs is constructed. The corresponding computer code is available on request. Finite-size effects in scale-free graphs, i.e. those where the tail of the degree distribution falls like nβn^{-\beta}, are carefully studied. We find that in the absence of dynamical internode correlations the degree distribution is cut at a degree value scaling like NγN^{\gamma}, with γ=min[1/2,1/(β1)]\gamma = \min[1/2, 1/(\beta-1)], where NN is the total number of nodes. The consequence is that, independently of any specific model, the inter-node correlations seem to be a necessary ingredient of the physics of scale-free networks observed in nature.

Keywords

Cite

@article{arxiv.cond-mat/0207020,
  title  = {Uncorrelated Random Networks},
  author = {Z. Burda and A. Krzywicki},
  journal= {arXiv preprint arXiv:cond-mat/0207020},
  year   = {2009}
}

Comments

7 pages, 4 eps figures, 2-column revtex format, final corrections before publication

R2 v1 2026-07-22T10:38:37.073Z