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 Concept Videos

Random Sampling Method01:09

Random Sampling Method

14.1K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
14.1K
Probability Distributions01:32

Probability Distributions

11.6K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
11.6K
Reynolds Transport Theorem01:24

Reynolds Transport Theorem

1.8K
The Reynolds transport theorem provides a framework to relate the time rate of change of an extensive property within a system to that in a control volume, which is crucial for analyzing fluid dynamics. Extensive properties, such as mass, velocity, acceleration, temperature, and momentum, can be expressed in terms of the mass of a fluid portion. These properties are called extensive because they depend on the system's size, while intensive properties are their corresponding values per unit...
1.8K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.8K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.8K
Random Error01:04

Random Error

7.7K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
7.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dynamics of individual active elastic filaments with chiral self-propulsion.

Physical review. E·2026
Same author

Dynamics of fluctuating thin sheets under random forcing.

Physical review. E·2023
Same author

Distribution of shortest path lengths in a class of node duplication network models.

Physical review. E·2018
See all related articles

Related Experiment Video

Updated: Jan 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K

Nonreciprocal random networks and their percolation properties.

Chanania Steinbock1

  • 1Johns Hopkins University, Department of Physics and Astronomy, Baltimore, Maryland 21218, USA.

Physical Review. E
|November 18, 2025
PubMed
Summary

We explored how nonreciprocity and network structure impact percolation in directed networks. Our findings show that node distinguishability significantly alters percolation properties, necessitating new analytical approaches for complex network analysis.

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.4K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

Related Experiment Videos

Last Updated: Jan 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.4K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

Area of Science:

  • Network Science
  • Statistical Physics

Background:

  • Percolation theory is crucial for understanding connectivity in complex systems.
  • Nonreciprocity in networks, where link probabilities differ in opposing directions, is common in real-world systems.
  • The interplay between network structure and nonreciprocity on percolation remains an active research area.

Purpose of the Study:

  • To investigate the effects of nonreciprocity and network structure on percolation phenomena.
  • To analytically determine the degree and percolation properties of nonreciprocal random networks.
  • To develop a generalizable method for solving percolation problems in both structured and unstructured networks.

Main Methods:

  • Analytical determination of degree and percolation properties for nonreciprocal random networks with two link probabilities.
  • Development of a novel technique using self-consistent integral and differential equations.
  • Simulation-based validation of analytical predictions.

Main Results:

  • Nonreciprocity and network structure profoundly influence percolation properties.
  • The statistical distinguishability of nodes significantly impacts quantitative measures and analytical approaches.
  • The developed method accurately predicts percolation behavior in diverse network types.

Conclusions:

  • Nonreciprocal network structure introduces unique challenges and opportunities for percolation analysis.
  • The proposed integral and differential equation method offers a robust framework for studying percolation in complex networks.
  • This work provides fundamental insights into the behavior of directed, nonreciprocal systems.