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

4.5K
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,...
4.5K
Protein Networks02:26

Protein Networks

2.9K
2.9K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
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...
16.2K
Density00:56

Density

19.9K
Density is an important characteristic of substances, crucial in determining whether an object sinks or floats in a fluid. Its SI unit is kg/m3, and its cgs unit is g/cm3. The density of an object helps in identifying its composition, and also reveals information about the phase of the matter and its substructure. The densities of liquids and solids are roughly comparable, consistent with the fact that their atoms are in close contact. However, gases have much lower densities than liquids and...
19.9K
Current Density01:21

Current Density

5.1K
The total amount of current flowing through one unit value of a cross-sectional area is referred to as current density. If the current flow is uniform, the amount of current flowing through a conductor is the same at all points along the conductor, even if the conductor area varies. The current density consists of the local magnitude and direction of the charge flow, which varies from point to point. Current density is measured in amperes per meter square, and direction is defined as the net...
5.1K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

728
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
728

You might also read

Related Articles

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

Sort by
Same author

Testing maximum entropy models with e-values.

Physical review. E·2026
Same author

Reconstruction of Multiplex Networks with Correlated Layers.

Entropy (Basel, Switzerland)·2026
Same author

Inferring firm-level supply chain networks with realistic systemic risk from industry sector-level data.

Scientific reports·2026
Same author

Renormalization of interacting random graph models.

Physical review. E·2026
Same author

Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Partial correlation as a tool for mapping functional-structural correspondence in human brain connectivity.

Network neuroscience (Cambridge, Mass.)·2025
Same journal

Changes in patient-sharing patterns after oncologist departures in rural and urban settings: a Medicare cohort study.

Applied network science·2026
Same journal

Tunable network properties with Hamill and Gilbert's Social Circles generator.

Applied network science·2025
Same journal

Initialisation and network effects in decentralised federated learning.

Applied network science·2025
Same journal

The association of prescriber prominence in a shared-patient physician network with their patients receipt of and transitions between risky drug combinations.

Applied network science·2025
Same journal

Accounting for contact network uncertainty in epidemic inferences with Approximate Bayesian Computation.

Applied network science·2025
Same journal

Navigation on temporal networks.

Applied network science·2025
See all related articles

Related Experiment Video

Updated: Feb 1, 2026

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
10:45

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

13.7K

Network reconstruction via density sampling.

Tiziano Squartini1, Giulio Cimini1,2, Andrea Gabrielli1,2

  • 11IMT School for Advanced Studies Lucca, Piazza S.Francesco 19, Lucca, 55100 Italy.

Applied Network Science
|December 12, 2018
PubMed
Summary
This summary is machine-generated.

This study presents a new method to reconstruct weighted networks even without knowing the total number of links. Randomly sampling nodes is key for accurate network reconstruction from limited data.

Keywords:
89.75.Hc; 89.65.Gh; 02.50.Tt

More Related Videos

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach
08:01

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Published on: August 24, 2018

9.5K
Ablation of Ischemic Ventricular Tachycardia Using a Multipolar Catheter and 3-dimensional Mapping System for High-density Electro-anatomical Reconstruction
06:57

Ablation of Ischemic Ventricular Tachycardia Using a Multipolar Catheter and 3-dimensional Mapping System for High-density Electro-anatomical Reconstruction

Published on: January 31, 2019

15.4K

Related Experiment Videos

Last Updated: Feb 1, 2026

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
10:45

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

13.7K
Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach
08:01

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Published on: August 24, 2018

9.5K
Ablation of Ischemic Ventricular Tachycardia Using a Multipolar Catheter and 3-dimensional Mapping System for High-density Electro-anatomical Reconstruction
06:57

Ablation of Ischemic Ventricular Tachycardia Using a Multipolar Catheter and 3-dimensional Mapping System for High-density Electro-anatomical Reconstruction

Published on: January 31, 2019

15.4K

Area of Science:

  • Network science
  • Statistical physics
  • Data science

Background:

  • Reconstructing weighted networks is crucial for applications like systemic risk estimation.
  • Accurate reconstruction often relies on known degree or strength sequences and total link counts, which are frequently unavailable.
  • Existing methods have limitations when observational data is scarce.

Purpose of the Study:

  • To develop a novel procedure for reconstructing weighted networks when even the total number of links is unknown.
  • To establish a reliable method for network topology and link weight reconstruction using limited information.
  • To address the challenge of network reconstruction under severe data constraints.

Main Methods:

  • Assuming network homogeneity, link density is estimated via sampling node subsets.
  • Demonstrating that random node selection is the optimal sampling strategy to avoid biased density estimations.
  • Introducing a core technique for reconstructing both network topology and link weights.

Main Results:

  • The proposed method accurately reconstructs weighted networks from partial information.
  • The technique is robust even when using small, randomly sampled subsets of nodes.
  • Performance validated on real-world economic and financial datasets.

Conclusions:

  • The developed method provides a reliable practical tool for network reconstruction with minimal available topological information.
  • It significantly relaxes observational requirements compared to previous approaches.
  • The findings are applicable in scenarios with restricted data, such as financial network analysis.