Generating structured networks based on a weight-dependent deactivation mechanism.
Motivated by the degree-dependent deactivation model generating networks with high clustering coefficient [K. Klemm, Phys. Rev. E. 65, 036123 (2002)], a weight-dependent version is studied to model evolving networks. The growth dynamics of the network is based on a naive weight-driven deactivation mechanism which couples the establishment of new active vertices and the weights' dynamical evolution. Both analytical solutions and numerical simulations show that the generated networks possess a high clustering coefficient larger than that for regular lattices of the same average connectivity. Weighted, structured scale-free networks are obtained as the deactivated vertex is target selected at each time step, and weighted, structured exponential networks are realized for the random-selected case.
Wu
Zhi-Xi
ZX
Institute of Theoretical Physics, Lanzhou University, Lanzhou Gansu 730000, China.
Xu
Xin-Jian
XJ
Wang
Ying-Hai
YH