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

119
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,...
119
Modeling and Similitude01:12

Modeling and Similitude

261
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
261
Dimensional Analysis01:27

Dimensional Analysis

315
Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena.
In fluid mechanics, dimensional...
315
Correlation of Experimental Data01:23

Correlation of Experimental Data

229
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
229
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.3K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.3K

You might also read

Related Articles

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

Sort by
Same author

Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours.

PloS one·2026
Same author

Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification.

Scientific reports·2026
Same author

A general framework for adaptive nonparametric dimensionality reduction.

Scientific reports·2026
Same author

Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks.

Advances in data analysis and classification·2025
Same author

Rethinking psychometrics through LLMs: how item semantics shape measurement and prediction in psychological questionnaires.

Scientific reports·2025
Same author

Linear Scaling Causal Discovery from High-Dimensional Time Series by Dynamical Community Detection.

Physical review letters·2025

Related Experiment Video

Updated: Jun 18, 2025

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

5.6K

Intrinsic dimension as a multi-scale summary statistics in network modeling.

Iuri Macocco1, Antonietta Mira2,3, Alessandro Laio4,5

  • 1International School for Advanced Studies (SISSA), Via Bonomea 265, 34136, Trieste, Italy.

Scientific Reports
|July 31, 2024
PubMed
Summary
This summary is machine-generated.

We introduce intrinsic dimension, a new network analysis method, to better understand complex systems across all scales. This approach aids in developing advanced models for network generation, especially for those with large diameters.

More Related Videos

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

1.0K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Related Experiment Videos

Last Updated: Jun 18, 2025

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

5.6K
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

1.0K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Area of Science:

  • Network science
  • Mathematical modeling
  • Complex systems analysis

Background:

  • Complex networks are crucial for understanding interconnected systems.
  • Current analysis methods focus on local or global properties, missing multi-scale structural insights.
  • Characterizing network structure across all scales remains a challenge.

Purpose of the Study:

  • Introduce a novel method to calculate the intrinsic dimension of unweighted networks.
  • Utilize intrinsic dimension as a summary statistic for mechanistic network models.
  • Develop a new model capable of reproducing intrinsic dimensions in networks with large diameters.

Main Methods:

  • Developed a rigorous method for calculating intrinsic dimension in unweighted networks.
  • Applied intrinsic dimension as a summary statistic within Approximate Bayesian Computation (ABC).
  • Proposed a new mechanistic model for complex network generation.

Main Results:

  • Intrinsic dimension effectively characterizes network structure across multiple scales.
  • The proposed ABC framework successfully infers parameters for mechanistic network models.
  • The new mechanistic model reproduces intrinsic dimensions of networks with large diameters.

Conclusions:

  • Intrinsic dimension offers a comprehensive measure of network structure across scales.
  • This method enhances the analysis and generation of complex networks.
  • The introduced model addresses limitations in generating networks with large diameters.