English

Synchronization on community networks

Disordered Systems and Neural Networks 2011-11-09 v2 Statistical Mechanics Physics and Society

Abstract

In this Letter, we propose a growing network model that can generate scale-free networks with a tunable community strength. The community strength, CC, is directly measured by the ratio of the number of external edges to internal ones; a smaller CC corresponds to a stronger community structure. According to the criterion obtained based on the master stability function, we show that the synchronizability of a community network is significantly weaker than that of the original Barab\'asi-Albert network. Interestingly, we found an unreported linear relationship between the smallest nonzero eigenvalue and the community strength, which can be analytically obtained by using the combinatorial matrix theory. Furthermore, we investigated the Kuramoto model and found an abnormal region (C0.002C\leq 0.002), in which the network has even worse synchronizability than the uncoupled case (C=0). On the other hand, the community effect will vanish when CC exceeds 0.1. Between these two extreme regions, a strong community structure will hinder global synchronization.

Keywords

Cite

@article{arxiv.cond-mat/0605024,
  title  = {Synchronization on community networks},
  author = {Tao Zhou and Ming Zhao and Guanrong Chen and Gang Yan and Bing-Hong Wang},
  journal= {arXiv preprint arXiv:cond-mat/0605024},
  year   = {2011}
}

Comments

4 figures and 4 pages