Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Nonlinear Sciences > Chaotic Dynamics

arXiv:nlin/0612057 (nlin)
[Submitted on 26 Dec 2006 (v1), last revised 23 Nov 2007 (this version, v2)]

Title:Optimization of synchronization in gradient clustered networks

Authors:Xingang Wang, Liang Huang, Ying-Cheng Lai, Choy Heng Lai
View PDF
Abstract: We consider complex clustered networks with a gradient structure, where sizes of the clusters are distributed unevenly. Such networks describe more closely actual networks in biophysical systems and in technological applications than previous models. Theoretical analysis predicts that the network synchronizability can be optimized by the strength of the gradient field but only when the gradient field points from large to small clusters. A remarkable finding is that, if the gradient field is sufficiently strong, synchronizability of the network is mainly determined by the properties of the subnetworks in the two largest clusters. These results are verified by numerical eigenvalue analysis and by direct simulation of synchronization dynamics on coupled-oscillator networks.
Comments: PRE, 76, 056113 (2007)
Subjects: Chaotic Dynamics (nlin.CD); Adaptation and Self-Organizing Systems (nlin.AO)
Cite as: arXiv:nlin/0612057 [nlin.CD]
  (or arXiv:nlin/0612057v2 [nlin.CD] for this version)
  https://doi.org/10.48550/arXiv.nlin/0612057
Related DOI: https://doi.org/10.1103/PhysRevE.76.056113

Submission history

From: Xingang Wang Dr [view email]
[v1] Tue, 26 Dec 2006 23:39:56 UTC (138 KB)
[v2] Fri, 23 Nov 2007 10:18:05 UTC (77 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
nlin.CD
< prev   |   next >
new | recent | 2006-12

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)