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

Circuit Terminology01:14

Circuit Terminology

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.
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Protein Networks02:26

Protein Networks

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,...
Protein Networks02:26

Protein Networks

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,...
Block Diagram Reduction01:22

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
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...

You might also read

Related Articles

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

Sort by
Same author

A digital archive reveals how a funding agency cooperated with academics to support the nascent field of genomics.

Nature communications·2026
Same author

Bayesian symbolic regression: automated equation discovery from a physicist's perspective.

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

ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings.

Briefings in bioinformatics·2026
Same author

Connections between physics and metabolism in brain functions.

iScience·2026
Same author

The evolution of interdisciplinarity and internationalization in scientific journals.

eLife·2025
Same author

Reply to Neveu and Neveu: Inference in an information-restricted environment.

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

Related Experiment Video

Updated: Jul 11, 2026

Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

Module identification in bipartite and directed networks.

Roger Guimerà1, Marta Sales-Pardo, Luís A Nunes Amaral

  • 1Northwestern Institute on Complex Systems (NICO) and Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 13, 2007
PubMed
Summary
This summary is machine-generated.

This study presents a novel approach for identifying modules in complex networks, specifically bipartite and directed unipartite networks. The method effectively represents directed networks as bipartite networks for robust module detection.

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: Jul 11, 2026

Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

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
  • Complex Systems Analysis
  • Graph Theory

Background:

  • Modularity is a key characteristic of complex networks, influencing their structure and function.
  • Identifying modules (communities) is crucial for understanding network organization.
  • Existing methods often struggle with specialized network types like bipartite and directed networks.

Purpose of the Study:

  • To develop and validate an effective module identification approach for bipartite networks.
  • To demonstrate the utility of representing directed unipartite networks as bipartite networks for module detection.
  • To provide a robust method for analyzing community structure in diverse network types.

Main Methods:

  • Developed a specialized module detection algorithm tailored for bipartite networks.
  • Introduced a novel representation of directed unipartite networks as bipartite networks.
  • Created a set of random networks for rigorous validation of the proposed module identification approach.

Main Results:

  • The proposed approach successfully identifies modules in bipartite networks.
  • Representing directed unipartite networks as bipartite networks facilitates their module analysis.
  • Validation using random networks confirms the robustness and accuracy of the method.

Conclusions:

  • The study offers a unified and effective strategy for module detection across bipartite and directed unipartite networks.
  • The findings contribute to a deeper understanding of community structures in complex systems.
  • This method enhances the analysis capabilities for various real-world network datasets.