English

Self-similar Scale-free Networks and Disassortativity

Statistical Mechanics 2009-11-11 v1 Disordered Systems and Neural Networks

Abstract

Self-similar networks with scale-free degree distribution have recently attracted much attention, since these apparently incompatible properties were reconciled in a paper by Song et al. by an appropriate box-counting method that enters the measurement of the fractal dimension. We study two genetic regulatory networks ({\it Saccharomyces cerevisiae} and {\it Escherichai coli} and show their self-similar and scale-free features, in extension to the datasets studied by Song et al. Moreover, by a number of numerical results we support the conjecture that self-similar scale-free networks are not assortative. From our simulations so far these networks seem to be disassortative instead. We also find that the qualitative feature of disassortativity is scale-invariant under renormalization, but it appears as an intrinsic feature of the renormalization prescription, as even assortative networks become disassortative after a sufficient number of renormalization steps.

Keywords

Cite

@article{arxiv.cond-mat/0507198,
  title  = {Self-similar Scale-free Networks and Disassortativity},
  author = {Soon-Hyung Yook and Filippo Radicchi and Hildegard Meyer-Ortmanns},
  journal= {arXiv preprint arXiv:cond-mat/0507198},
  year   = {2009}
}

Comments

4 pages, 6 figures