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

Mixing patterns in networks.

M E J Newman1

  • 1Department of Physics, University of Michigan, Ann Arbor, MI 48109-1120, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 15, 2003
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

Mutual information and the encoding of contingency tables.

Physical review. E·2025
Same author

Luck, skill, and depth of competition in games and social hierarchies.

Science advances·2024
Same author

Hierarchical core-periphery structure in networks.

Physical review. E·2023
Same author

Clustering of heterogeneous populations of networks.

Physical review. E·2022
Same author

Reconstruction of plant-pollinator networks from observational data.

Nature communications·2021
Same author

Belief propagation for networks with loops.

Science advances·2021
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

Assortative mixing, where similar network nodes connect, is common across diverse real-world networks. Varying this mixing level significantly impacts network connectivity and resilience, especially for degree assortativity.

Area of Science:

  • Network Science
  • Graph Theory
  • Sociology

Background:

  • Assortative mixing describes the tendency of nodes in a network to connect with similar or dissimilar nodes.
  • This phenomenon applies to discrete characteristics (e.g., language, race) and scalar characteristics (e.g., age, degree).

Purpose of the Study:

  • To quantify and analyze assortative mixing across various network types and characteristics.
  • To investigate the impact of varying assortativity levels on network properties, particularly connectivity and resilience.

Main Methods:

  • Development of novel measures for quantifying assortative mixing across different characteristic types.
  • Application of these measures to diverse real-world networks.
  • Construction of analytical (generating functions) and numerical (Monte Carlo) models for assortatively mixed networks.

Related Experiment Videos

Main Results:

  • Assortative mixing is a pervasive phenomenon observed across numerous real-world networks.
  • Significant variations in network connectivity and resilience were found with changes in assortativity, especially for degree-based mixing.

Conclusions:

  • Assortative mixing is a fundamental property influencing network structure and function.
  • Understanding and modeling assortativity is crucial for predicting network behavior, robustness, and dynamics.