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

Particle-cluster aggregation on a small-world network.

Sheng-You Huang1, Xian-Wu Zou, Zhi-Gang Shao

  • 1Department of Physics, Wuhan University, Wuhan 430072, People's Republic of China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|July 13, 2004
PubMed
Summary

This study models particle aggregation on small-world networks, revealing how clustering and long-range connections influence growth patterns. Fractal dimensions shift from diffusion-limited aggregation-like to dense growth as clustering decreases.

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

DeepSSInter: Protein-protein contact prediction with a structure-aware protein language model.

Protein science : a publication of the Protein Society·2026
Same author

EMReady2: improvement of cryo-EM and cryo-ET maps by local quality-aware deep learning with Mamba.

Nature communications·2026
Same author

Structural atlas of the intact jumbo phage phiKZ.

Nature communications·2026
Same author

TriRNASP: A knowledge-based potential with three-body effects for accurate RNA structure evaluation.

Biophysical journal·2026
Same author

MetalKB: Predicting Metal Binding Sites on Proteins with a Knowledge-Based Graph Framework.

Journal of chemical information and modeling·2026
Same author

Local cation-clamping distorts and softens RNA duplex.

Communications biology·2026

Area of Science:

  • Complex systems
  • Network science
  • Statistical physics

Background:

  • Particle aggregation phenomena are crucial in various scientific fields.
  • Understanding aggregation on complex networks requires models that capture network topology.
  • Previous models often simplify network structures, limiting applicability to real-world systems.

Purpose of the Study:

  • To introduce and analyze a model for particle-cluster aggregation on a 2D small-world network.
  • To investigate the impact of clustering exponent (alpha) and long-range connection rate (phi) on aggregation behavior.
  • To determine how network properties affect the resulting fractal dimension.

Main Methods:

  • Development of a particle-cluster aggregation model on a 2D small-world network.

Related Experiment Videos

  • Characterization of the model using two key parameters: clustering exponent (alpha) and long-range connection rate (phi).
  • Analysis of asymptotic and effective fractal dimensions using primitive analysis.

    • Main Results:

    • A crossover in aggregation patterns from diffusion-limited aggregation-like to dense growth was observed as alpha decreased.
    • The asymptotic fractal dimension D(max)(f) was found to depend on alpha, ranging from 1.7 to 2.0.
    • Long-range connections were shown to reduce screening effects, altering aggregation patterns.
    • A finite-size effect was identified, where the effective fractal dimension D(f) depends on phi for smaller systems.

    Conclusions:

    • The study provides a model for aggregation on networks with long-range jump paths.
    • Network parameters alpha and phi significantly influence aggregation dynamics and fractal dimensions.
    • An expression for the effective fractal dimension D(f) based on alpha and phi was derived, offering insights into finite-size effects.