A rank-dependent deactivation mechanism is introduced to network evolution.
The growth dynamics of the network is based on a finite memory of individuals,
which is implemented by deactivating one site at each time step. The model
shows striking features of a wide range of real-world networks: power-law
degree distribution, high clustering coefficient, and disassortative degree
correlation.Comment: 5 pages, 5 figures, RevTex

Xu, Xin-Jian