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

Protein Networks02:26

Protein Networks

3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.9K
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

768
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
768
Weighted Mean00:57

Weighted Mean

5.1K
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...
5.1K
Network Function of a Circuit01:25

Network Function of a Circuit

290
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
290
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

103
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
103
Network Covalent Solids02:18

Network Covalent Solids

13.5K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
13.5K

You might also read

Related Articles

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

Sort by
Same author

Modelling the role of the microbiome in antimicrobial resistance across scales.

Nature microbiology·2026
Same author

Agreement between seroprevalence- and model-based estimates of COVID-19 burden.

Global health action·2026
Same author

Learning dynamical systems with biochemically informed neural ordinary differential equations.

bioRxiv : the preprint server for biology·2026
Same author

Bounded-confidence opinion models with random-time interactions.

Physical review. E·2026
Same author

Clustering-induced localization of quantum walks on networks.

Physical review. E·2026
Same author

Bounded-confidence models of opinion dynamics with neighborhood effects.

Physical review. E·2025
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Jun 30, 2025

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

1.1K

Complex networks with complex weights.

Lucas Böttcher1,2, Mason A Porter3,4,5

  • 1Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, 60322 Frankfurt am Main, Germany.

Physical Review. E
|March 16, 2024
PubMed
Summary
This summary is machine-generated.

This study explores complex-weighted networks, showing standard analysis methods fail. Generalized methods, particularly random-walk centralities, effectively reveal node importance in these intricate network structures.

More Related Videos

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

2.1K
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

4.3K

Related Experiment Videos

Last Updated: Jun 30, 2025

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

1.1K
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

2.1K
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

4.3K

Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Quantum Information Theory

Background:

  • Traditional network analysis often uses binary (unweighted) or real-valued weighted edges.
  • Networks with complex-valued weights are prevalent in quantum information, chemistry, and machine learning.
  • Existing network science methods are typically designed for real-valued weights and may not capture complex network features.

Purpose of the Study:

  • To investigate the limitations of standard network analysis methods when applied to networks with complex edge weights.
  • To generalize established network measures to accommodate complex-valued weights.
  • To identify robust methods for analyzing node importance in complex-weighted networks.

Main Methods:

  • Examination of how conventional network analysis techniques perform on networks with complex edge weights.
  • Generalization of several key network measures to the complex domain.
  • Application and evaluation of random-walk centralities for analyzing node importance in complex-weighted networks.

Main Results:

  • Standard network analysis methods often fail to accurately represent the structural properties of complex-weighted networks.
  • Generalized network measures are developed to handle complex edge weights effectively.
  • Random-walk centralities demonstrate significant utility in determining node importance within complex-weighted network structures.

Conclusions:

  • There is a critical need to adapt network analysis tools for complex-valued weights.
  • Generalized network measures provide a pathway to analyze previously intractable network types.
  • Random-walk centralities offer a promising approach for node importance analysis in complex-weighted networks.