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

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Random Sampling Method01:09

Random Sampling Method

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...
Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...

You might also read

Related Articles

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

Sort by
Same author

Complex network topological and spectral determinants of extreme events.

Chaos (Woodbury, N.Y.)·2026
Same author

Introduction to Focus Issue: Nonautonomous dynamical systems: Theory, methods, and applications.

Chaos (Woodbury, N.Y.)·2026
Same author

Transcript-based estimators for characterizing interactions.

Chaos (Woodbury, N.Y.)·2026
Same author

Noise Robustness of Transcript-Based Estimators for Properties of Interactions.

Entropy (Basel, Switzerland)·2025
Same author

Functional Importance Backbones of the Brain at Rest, Wakefulness, and Sleep.

Brain sciences·2025
Same author

Introduction to Focus Issue: Data-driven models and analysis of complex systems.

Chaos (Woodbury, N.Y.)·2025

Related Experiment Video

Updated: May 29, 2026

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

Constrained randomization of weighted networks.

Gerrit Ansmann1, Klaus Lehnertz

  • 1Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, D-53105 Bonn, Germany. gansmann@uni-bonn.de

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 21, 2011
PubMed
Summary

We developed a Markov chain method to create random surrogate networks that preserve specific properties. This approach helps reveal unique characteristics of complex networks like brain and trade networks.

Related Experiment Videos

Last Updated: May 29, 2026

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

Area of Science:

  • Network Science
  • Computational Neuroscience
  • Economic Networks

Background:

  • Real-world networks, such as functional brain networks and international trade networks, exhibit complex structures.
  • Understanding the specific properties of these networks is crucial for various scientific disciplines.
  • Existing methods for generating random networks may not preserve all essential network characteristics.

Purpose of the Study:

  • To introduce an efficient Markov chain method for generating surrogate networks.
  • To preserve network properties like vertex strengths and edge weights during randomization.
  • To utilize these surrogates for a deeper interpretation of empirical weighted networks.

Main Methods:

  • Development of a Markov chain Monte Carlo (MCMC) approach.
  • Generation of strength-preserving surrogate networks.
  • Generation of edge-weight-preserving surrogate networks.
  • Analysis of clustering coefficient and average shortest path length.

Main Results:

  • The proposed Markov chain method efficiently generates valid surrogate networks.
  • Surrogate networks reveal network-specific characteristics not apparent in unweighted or unconstrained random networks.
  • Analysis of human brain functional networks and international trade networks using these surrogates.

Conclusions:

  • Surrogate network generation is a valuable tool for network analysis.
  • This method enhances the interpretation of empirical weighted networks.
  • The approach provides insights into the structural properties of complex systems.