2006/08/16 by Xingang Wang, Wang, Xingang, Ying‐Cheng Lai +3
Computer Science · Engineering · #Chaotic Dynamics (nlin.CD) #FOS: Physical sciences #Molecular Communication and Nanonetworks #Neural Networks Stability and Synchronization #Nonlinear Dynamics and Pattern Formation
paper · pdf · doi:10.48550/arxiv.nlin/0608035
openalex publication_date 2006/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The contradiction between the fact that many empirical networks possess power-law degree distribution and the finding that network of heterogeneous degree distribution is difficult to synchronize has been a paradox in the study of network synchronization. Surprisingly, we find that this paradox can be well fixed when proper gradients are introduced to the network links, i.e. heterogeneity is in favor of synchronization in gradient networks. We analyze the statistical properties of gradient networks and explore their dependence to the other network parameters. Based on these understandings, we further propose a new scheme for network synchronization distinguished by using less network information while reaching stronger synchronizability, as supported by analytical estimates of eigenvalues and directed simulations of coupled chaotic oscillators. Our findings suggest that, with gradient, scale-free network is a natural choice for synchronization.