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

Stochastically evolving networks.

Derek Y C Chan1, Barry D Hughes, Alex S Leong

  • 1Particulate Fluids Processing Centre, Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010, Australia. D.Chan@ms.unimelb.edu.au

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 3, 2004
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

Kinetic and Kinematic Effects of Flag-Carrying on Change of Direction and Sprinting Performance in Assistant Referees.

Journal of strength and conditioning research·2026
Same author

Temporal variability and predictability predict alpine plant community composition and distribution patterns.

Ecology·2024
Same author

Distributed Estimation of Fields Using a Sensor Network with Quantized Measurements.

Sensors (Basel, Switzerland)·2024
Same author

Variational solution to the lattice Boltzmann method for Couette flow.

Physical review. E·2024
Same author

Dynamic interactions between drops-a critical assessment.

Soft matter·2020
Same author

Soft matter: from shapes to forces on the nanoscale.

Soft matter·2020
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

This study introduces network evolution models where nodes and links form randomly over time. Some models generate small-world networks and exhibit power-law distributions, applicable to biological and communication systems.

Area of Science:

  • Network Science
  • Mathematical Modeling
  • Complex Systems

Background:

  • Understanding network evolution is crucial for various applications, from biological systems to social interactions.
  • Existing models often focus on specific network structures or formation rules.
  • A unified framework for diverse network growth patterns is needed.

Purpose of the Study:

  • To introduce and analyze a flexible class of network evolution models.
  • To explore models generating both tree-like and cyclic network structures.
  • To investigate the emergence of specific network properties like small-world phenomena and power-law distributions.

Main Methods:

  • Development of a class of random network growth models.
  • Analytical techniques including exact results for moments and distributions.

Related Experiment Videos

  • Computational simulations and mean-field approximations.
  • Exploitation of results from related models, such as random recursive trees.

    • Main Results:

    • Models producing both tree structures and networks with cycles (small-world and less connected).
    • Exact results for network size, coordination number moments, correlations, and distributions.
    • Identification of conditions leading to power-law distributions in long-evolved systems.
    • Comparison of analytical predictions with simulation outcomes.

    Conclusions:

    • The proposed models offer a versatile framework for studying network evolution.
    • Random node and link formation can lead to diverse network topologies and properties.
    • Power-law distributions emerge in certain models under long-term evolution, relevant for real-world systems.