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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

419
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
419
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

313
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
313
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

285
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...
285
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

214
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...
214
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

212
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
212
Three-Compartment Open Model01:06

Three-Compartment Open Model

731
The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
731

You might also read

Related Articles

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

Sort by
Same author

Anatomy of a Swedish population-scale network.

Scientific reports·2025
Same author

Visual digital intermediaries and global climate communication: Is climate change still a distant problem on YouTube?

PloS one·2025
Same author

Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts.

IEEE transactions on visualization and computer graphics·2024
Same author

Immigrant-critical alternative media in online conversations.

PloS one·2023
Same author

Ecological Adaptation and Succession of Human Fecal Microbial Communities in an Automated <i>In Vitro</i> Fermentation System.

mSystems·2021
Same author

An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks.

Frontiers in big data·2021
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Dec 15, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

898

Unspoken Assumptions in Multi-layer Modularity maximization.

Obaida Hanteer1, Matteo Magnani2

  • 1IT University of Copenhagen, Copenhagen, 2300, Denmark. obaida.hanteer@hotmail.com.

Scientific Reports
|July 8, 2020
PubMed
Summary
This summary is machine-generated.

Community detection in multi-layer networks using generalized modularity is limited. While simple structures are recoverable, complex models are not accurately detected, regardless of the coupling strength (ω) tuning.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.6K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.3K

Related Experiment Videos

Last Updated: Dec 15, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

898
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.6K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.3K

Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Community detection in networks aims to identify densely connected groups of nodes.
  • Generalized modularity extends this concept to multi-layer networks using a coupling strength (ω) parameter.
  • The interpretability and effectiveness of ω for diverse multi-layer community structures remain unclear.

Purpose of the Study:

  • To investigate the types of communities identified by maximizing generalized modularity in multi-layer networks.
  • To analyze the impact of the coupling strength (ω) on community recovery.
  • To assess the recoverability of different multi-layer community models in multiplex and time-dependent networks.

Main Methods:

  • Reviewing literature on tuning the coupling strength (ω) parameter.
  • Analyzing community structures recoverable by maximizing generalized modularity concerning ω.
  • Proposing multi-layer community models for multiplex and time-dependent networks.
  • Testing the recoverability of these models using modularity-maximization methods across various ω assignments.

Main Results:

  • Generalized modularity maximization recovers only a limited set of simple multi-layer community structures.
  • Complex multi-layer community models in multiplex and time-dependent networks are not accurately recoverable.
  • The effectiveness of ω tuning is insufficient for capturing all possible multi-layer community structures.

Conclusions:

  • Modularity maximization with generalized modularity is effective for simple multi-layer communities but struggles with complex structures.
  • The coupling strength (ω) parameter does not universally guarantee accurate recovery of all multi-layer community types.
  • Further research is needed to develop methods capable of detecting diverse community structures in complex multi-layer networks.