Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Optimization in gradient networks.

Natali Gulbahce1

  • 1Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, MS B284, Los Alamos, New Mexico 87545, USA. gulbahce@lanl.gov

Chaos (Woodbury, N.Y.)
|July 7, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Donor-derived posttransplant lymphoproliferative disease detection by donor-derived cell-free DNA.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons·2024
Same author

Nuances in the interpretation and utility of donor-derived cell-free DNA in lung transplantation following allogeneic hematopoietic stem cell transplantation - Case report.

Transplant immunology·2024
Same author

High levels of donor-derived cell-free DNA in a case of graft-versus-host-disease following liver transplantation.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons·2021
Same author

Advanced Whole-Genome Sequencing and Analysis of Fetal Genomes from Amniotic Fluid.

Clinical chemistry·2018
Same author

Quantitative Whole Genome Sequencing of Circulating Tumor Cells Enables Personalized Combination Therapy of Metastatic Cancer.

Cancer research·2017
Same author

Extensive sequencing of seven human genomes to characterize benchmark reference materials.

Scientific data·2016
Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026
Same journal

State estimation in spatiotemporal chaos via low-rank StatFEM.

Chaos (Woodbury, N.Y.)·2026
Same journal

Universal response functions in driven dissipative tunneling dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A network-based approach to characterize the dynamics of the coupling field of thermoacoustic oscillators in annular geometry.

Chaos (Woodbury, N.Y.)·2026
Same journal

Data-driven soliton manifold approximations for dark and bright waves: Some prototypical 1D case examples.

Chaos (Woodbury, N.Y.)·2026
Same journal

Gap junction architecture and synchronization clusters in the thalamic reticular nuclei.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Researchers optimized gradient networks to minimize congestion on complex networks. This approach introduces structural correlations, significantly reducing network jamming for improved transport dynamics.

Area of Science:

  • Network science
  • Complex systems analysis
  • Computational physics

Background:

  • Gradient networks model complex network structures.
  • Prior research focused on random gradient networks.
  • Network congestion poses challenges for transport and dynamics.

Purpose of the Study:

  • To investigate gradient networks that minimize jamming on scale-free and Erdos-Renyi substrate networks.
  • To introduce structural correlations to reduce network congestion.
  • To optimize network properties for efficient transport processes.

Main Methods:

  • Studied gradient networks on scale-free and Erdos-Renyi structures.
  • Employed a Monte Carlo optimization scheme.
  • Introduced structural correlations to mitigate congestion.

Related Experiment Videos

Main Results:

  • Successfully minimized jamming on complex networks.
  • Significantly reduced network congestion through optimization.
  • Altered degree distribution and structural properties of gradient networks.

Conclusions:

  • Optimized gradient networks effectively reduce congestion.
  • Structural correlations are key to minimizing jamming.
  • Findings are relevant for transport and dynamical processes in real-world networks.