  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.
