Rank-based model for weighted network with hierarchical organization and disassortative mixing

TL;DR

This paper introduces a rank-based model for weighted network evolution that combines topological growth and weight dynamics, revealing scale-free distributions and a structural phase transition at a critical parameter value.

Contribution

It presents a novel rank-based network model that captures hierarchical organization and disassortative mixing, aligning with properties of biological networks.

Findings

Networks exhibit scale-free degree, strength, and weight distributions.

A structural phase transition occurs at alpha=1.

Networks show hierarchical organization and disassortative mixing for alpha>1.

Abstract

Motivated by a recently introduced network growth mechanism that rely on the ranking of node prestige measures [S. Fortunato \emph{et al}., Phys. Rev. Lett. \textbf{96}, 218701 (2006)], a rank-based model for weighted network evolution is studied. The evolution rule of the network is based on the ranking of node strength, which couples the topological growth and the weight dynamics. Both analytical solutions and numerical simulations show that the generated networks possess scale-free distributions of degree, strength, and weight in the whole region of the growth dynamics parameter ($\alpha>0$). We also characterize the clustering and correlation properties of this class of networks. It is showed that at $\alpha=1$ a structural phase transition occurs, and for $\alpha>1$ the generated network simultaneously exhibits hierarchical organization and disassortative degree correlation, which is consistent with a wide range of biological networks.