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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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...
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

You might also read

Related Articles

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

Sort by
Same author

Large language models and emergence: a complex systems perspective.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

Constructing stability: optimal learning in noisy ecological niches.

Proceedings. Biological sciences·2024
Same author

Symmetry-simplicity, broken symmetry-complexity.

Interface focus·2023
Same author

The debate over understanding in AI's large language models.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

Institutional dynamics and learning networks.

PloS one·2022
Same author

Introduction to the special issue: quantifying collectivity.

Theory in biosciences = Theorie in den Biowissenschaften·2021
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: May 9, 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

A family of algorithms for computing consensus about node state from network data.

Eleanor R Brush1, David C Krakauer, Jessica C Flack

  • 1Program in Quantitative and Computational Biology, Princeton University, Princeton, New Jersey, USA. brush@princeton.edu

Plos Computational Biology
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

Network analysis algorithms quantify consensus for node scoring. Breadth-based methods excel in accuracy and error robustness, while depth-based methods are crucial for indirect information like reputation, with caveats for error sensitivity.

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

Related Experiment Videos

Last Updated: May 9, 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

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

Area of Science:

  • Network Science
  • Computational Biology
  • Social Network Analysis

Background:

  • Nodes in biological and social networks vary in their contribution to network structure and function.
  • Existing algorithms for node scoring lack a clear mechanistic understanding.
  • These algorithms are vital for applications like website ranking and identifying critical species.

Purpose of the Study:

  • To elucidate the unifying mechanistic basis of network node scoring algorithms.
  • To investigate how different consensus-quantifying approaches (breadth vs. depth) impact node scoring accuracy.
  • To assess algorithm robustness against systematic errors and biases in network data.

Main Methods:

  • Analysis of algorithms that quantify network consensus through node connections (breadth) or net flow (depth).
  • Empirical testing using data from communication, social, and biological networks.
  • Evaluation of scoring accuracy using independent data and assessment of sensitivity to source biases.

Main Results:

  • A unifying property of scoring algorithms is their quantification of network consensus.
  • Breadth-based algorithms, derived from information theory, accurately score nodes and are robust to errors.
  • Depth-based algorithms, like Eigenvector Centrality, are necessary for scoring based on indirect information but can be sensitive to errors unless networks are transitive or assortative.

Conclusions:

  • The choice between breadth and depth algorithms depends on the network's structure and the nature of information used for scoring.
  • Understanding consensus quantification provides a mechanistic basis for algorithm performance.
  • Cognitive and computational demands of algorithms are critical considerations for decision-making systems.