Rank-dependent deactivation in network evolution
Physics and Society
2015-05-14 v1
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.
Cite
@article{arxiv.0912.1400,
title = {Rank-dependent deactivation in network evolution},
author = {Xin-Jian Xu and Ming-Chen Zhou},
journal= {arXiv preprint arXiv:0912.1400},
year = {2015}
}
Comments
5 pages, 5 figures, RevTex4