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

SFG Algebra01:16

SFG Algebra

394
In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
394
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

1.0K
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
1.0K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

648
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
648

You might also read

Related Articles

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

Sort by
Same author

Integrating in vitro aerosol characterization and cross-species PBPK modeling to predict human lung exposure of inhaled therapeutic proteins.

International journal of pharmaceutics: X·2026
Same author

Tucidinostat Plus R-CHOP vs R-CHOP in MYC/BCL2 Double-Expressor Diffuse Large B-Cell Lymphoma: A Randomized Clinical Trial.

JAMA·2026
Same author

PhageCGRNet: Integrating Chaos Game Representation of Genomes with Convolutional Neural Network for accurate phage host classification prediction.

PLoS computational biology·2026
Same author

Phase 2 study of relmacabtagene autoleucel (CD19 CAR-T) for relapsed/refractory mantle cell lymphoma in Chinese adults.

Blood advances·2026
Same author

Lipid metabolism disorders and osteoarthritis progression: Potential intervention with plant active ingredients.

World journal of orthopedics·2026
Same author

Prediction of vancomycin exposure in patients with central nervous system infections using physiologically based pharmacokinetic modeling.

Journal of pharmaceutical sciences·2026

Related Experiment Video

Updated: Mar 29, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.9K

Multifractal analysis of weighted networks by a modified sandbox algorithm.

Yu-Qin Song1,2, Jin-Long Liu1, Zu-Guo Yu1,3

  • 1Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, Hunan 411105, China.

Scientific Reports
|December 5, 2015
PubMed
Summary

A new sandbox weighted (SBw) algorithm efficiently analyzes multifractal properties in weighted complex networks. This method reveals how edge-weights influence multifractality in both theoretical and real-world collaboration networks.

More Related Videos

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

1.0K
Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
06:55

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

Published on: August 5, 2016

8.6K

Related Experiment Videos

Last Updated: Mar 29, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.9K
Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

1.0K
Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
06:55

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

Published on: August 5, 2016

8.6K

Area of Science:

  • Complex systems analysis
  • Network science
  • Fractal geometry

Background:

  • Multifractal analysis (MFA) systematically describes spatial heterogeneity in fractal patterns.
  • Existing MFA algorithms are primarily for unweighted complex networks.
  • The sandbox (SB) algorithm is a recent approach for unweighted network MFA.

Purpose of the Study:

  • To propose a modified sandbox algorithm (SBw) for multifractal analysis of weighted networks.
  • To investigate the multifractal properties of weighted fractal networks (WFNs) using the SBw algorithm.
  • To examine the influence of edge-weights on fractal dimensions in WFNs and real-world networks.

Main Methods:

  • Development of the sandbox weighted (SBw) algorithm for MFA of weighted networks.
  • Application of the SBw algorithm to "Sierpinski" and "Cantor dust" weighted fractal networks.
  • Analysis of multifractal properties in real-world collaboration networks using the SBw algorithm.

Main Results:

  • The SBw algorithm demonstrates efficiency and feasibility for MFA of weighted networks.
  • The fractal dimension and generalized fractal dimensions of WFNs are shown to vary with edge-weights.
  • Multifractality is confirmed in real-world collaboration networks and found to be influenced by edge-weights.

Conclusions:

  • The SBw algorithm is a viable tool for multifractal analysis of weighted complex networks.
  • Edge-weights play a significant role in determining the multifractal characteristics of networks.
  • The findings contribute to a deeper understanding of the structure and properties of weighted networks.