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

Scale-free multicomponent growing networks.

Jianhong Ke1

  • 1School of Physics and Electronic Information, Wenzhou Normal College, Wenzhou 325027, China. kejianhong@yahoo.com.cn

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|April 20, 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

Protective effect of <i>Peucedanum praeruptorum</i> Dunn extract on oxidative damage of LLC‑PK1 cells induced by H<sub>2</sub>O<sub>2</sub>.

Experimental and therapeutic medicine·2023
Same author

Cooperative behavior in evolutionary snowdrift games with the unconditional imitation rule on regular lattices.

Physical review. E, Statistical, nonlinear, and soft matter physics·2012
Same author

Polymer growth through radical polymerization and termination.

Physical review. E, Statistical, nonlinear, and soft matter physics·2009
Same author

Competition between the catalyzed birth and death in the exchange-driven growth.

Physical review. E, Statistical, nonlinear, and soft matter physics·2007
Same author

Kinetics of migration-driven aggregation processes on scale-free networks.

Physical review. E, Statistical, nonlinear, and soft matter physics·2007
Same author

Mutually catalyzed birth of population and assets in exchange-driven growth.

Physical review. E, Statistical, nonlinear, and soft matter physics·2006
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

We introduce a multicomponent growing network model with distinct node types and inter-type links. This model generates scale-free networks, unlike random networks with exponential distributions.

Area of Science:

  • Complex networks
  • Network science
  • Statistical physics

Background:

  • Growing network models are crucial for understanding complex systems.
  • Existing models often lack the heterogeneity found in real-world networks.
  • Multicomponent networks with specific link constraints are underexplored.

Purpose of the Study:

  • To propose and analyze a novel multicomponent growing network model.
  • To investigate the impact of inter-type links on network topology.
  • To determine the degree distributions of such networks.

Main Methods:

  • Development of a multicomponent growing network model with two node types and mandatory inter-type links.
  • Introduction of a new node linked to a pre-existing node of the other type.

Related Experiment Videos

  • Application of rate equations to analyze network connectivity and degree distributions.

    • Main Results:

    • The proposed model generates networks with scale-free power-law degree distributions under specific connection rate kernels.
    • Networks with shifted or asymptotically linear connection rate kernels exhibit scale-free properties.
    • In contrast, a standard random growing network model results in exponential degree distributions.

    Conclusions:

    • The multicomponent growing network model effectively produces scale-free network structures.
    • The specific rules for node and link addition are key to achieving scale-free properties.
    • This model offers a new framework for studying heterogeneous complex systems.