title: Rank-dependent deactivation in network evolution.
abstract: 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.
authors.affiliation: Department of Mathematics, College of Science, Shanghai University, Shanghai, China. xinjxu@shu.edu.cn
authors.forename: Xin-Jian
authors.initials: XJ
authors.lastname: Xu
authors.forename: Ming-Chen
authors.initials: MC
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003195:Computer Communication Networks; D003198:Computer Simulation; D006801:Humans; D008433:Mathematics; D008568:Memory; D015233:Models, Statistical