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

Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

202
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
202
Circuit Terminology01:14

Circuit Terminology

2.3K
An electrical network is a system composed of interconnected elements, such as resistors, capacitors, inductors, and voltage or current sources. Unlike a circuit, an electrical network does not necessarily form a closed path. In other words, while all circuits can be considered networks due to their interconnected nature, not every network qualifies as a circuit.
A circuit, on the other hand, is also an interconnected system of electrical elements but must contain one or more closed paths.
2.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

110
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
110
Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K
Classification of Systems-I01:26

Classification of Systems-I

362
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
362
Protein Networks02:26

Protein Networks

4.2K
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,...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Optimal ambition in business, politics, and life.

Physical review. E·2026
Same author

Riparian vegetation reduces coastal turbidity.

Communications sustainability·2026
Same author

Dynamic and context-dependent keystone species effects in kelp forests.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Rebound effects could offset more than half of avoided food loss and waste.

Nature food·2023
Same author

Good and bad news for ocean predators.

Science (New York, N.Y.)·2022
Same author

Catastrophic climate risks should be neither understated nor overstated.

Proceedings of the National Academy of Sciences of the United States of America·2022
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Oct 16, 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

1.2K

Empirically classifying network mechanisms.

Ryan E Langendorf1, Matthew G Burgess2,3,4

  • 1Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, 216 UCB, Boulder, CO, 80309, USA. ryan.langendorf@colorado.edu.

Scientific Reports
|October 16, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to classify network data, revealing that most real-world networks may not follow common generating mechanisms. Many networks also appear to be governed by multiple, potentially unidentifiable, mechanisms.

More Related Videos

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

757
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.8K

Related Experiment Videos

Last Updated: Oct 16, 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

1.2K
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

757
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.8K

Area of Science:

  • Network science
  • Computational biology
  • Statistical modeling

Background:

  • Network data analysis often relies on assumed generative models.
  • Lack of validation for these models can lead to inaccurate conclusions.
  • Assessing the fit of empirical networks to theoretical mechanisms is crucial.

Purpose of the Study:

  • To develop an empirical approach for classifying network data based on candidate generative mechanisms.
  • To evaluate the prevalence of common network mechanisms in real-world systems.
  • To investigate the possibility of multiple mechanisms governing single networks.

Main Methods:

  • Developed a novel empirical classification method for network data.
  • Tested the method on simulated data from five well-established network mechanisms.
  • Applied the method to 1284 empirical networks across 17 system types.

Main Results:

  • The classification approach demonstrated high accuracy on simulated data.
  • 30% of empirical networks did not fit any of the five tested mechanisms.
  • Only a small fraction (≤1%) of networks classified as a specific mechanism, suggesting potential false positives.
  • 7% of networks showed characteristics of multiple mechanisms, indicating potential mechanism mixtures.

Conclusions:

  • Most empirical networks may not be accurately described by the five widely studied mechanisms.
  • The high rate of unclassified networks suggests limitations in current network generation models.
  • Some systems may be governed by a combination of mechanisms, though these mixtures can be difficult to identify.
  • Despite unidentifiable mixtures, functional properties of networks can still be predicted accurately.