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

Preferential urn model and nongrowing complex networks.

Jun Ohkubo1, Muneki Yasuda, Kazuyuki Tanaka

  • 1Department of System Information Sciences, Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai 980-8579, Japan. jun@smapip.is.tohoku.ac.jp

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 21, 2006
PubMed
Summary

A new preferential urn model, based on "the rich get richer," explains complex network degree distributions. Local temperature concepts intuitively clarify fat-tailed distributions in this stochastic model.

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

Comprehension of medication-related information translated from Japanese to Southeast Asian Languages using google translate.

Journal of pharmaceutical health care and sciences·2026
Same author

Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization.

PloS one·2025
Same author

Educational interventions to enhance support for balancing work and treatment in inflammatory bowel disease patients.

Journal of gastroenterology·2025
Same author

Comparison of traction vs. snare as rescue methods for challenging colorectal endoscopic submucosal dissection: Propensity score-matched study.

Endoscopy international open·2025
Same author

Dataset-free weight-initialization on restricted Boltzmann machine.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

High-resolution genetic analysis of whole APC gene deletions: a report of two cases and patient characteristics.

Human genome variation·2024

Area of Science:

  • Complex Systems
  • Statistical Physics
  • Network Science

Background:

  • Preferential attachment models, like "the rich get richer," are used to study complex systems.
  • Understanding degree distributions in nongrowing complex networks is crucial for network analysis.

Purpose of the Study:

  • To propose a preferential urn model that explains degree distributions in complex networks.
  • To establish a relationship between network fitness parameters and the preferential urn model's temperature.
  • To intuitively explain fat-tailed occupation distributions using the concept of local temperature.

Main Methods:

  • Development of a preferential urn model incorporating randomness.
  • Analysis of the relationship between the proposed model and nongrowing complex network models.

Related Experiment Videos

  • Interpretation of the fitness parameter as an inverse local temperature.

    • Main Results:

    • The preferential urn model successfully explains degree distributions in complex networks.
    • A fitness parameter in network models is equivalent to inverse local temperature in the urn model.
    • The model generates fat-tailed occupation distributions, intuitively explained by local temperature.

    Conclusions:

    • The preferential urn model provides a simple stochastic framework for understanding complex network properties.
    • The concept of local temperature offers an intuitive explanation for fat-tailed distributions.
    • The model has broad applicability in complex networks, econophysics, and social sciences.