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

Network Function of a Circuit01:25

Network Function of a Circuit

290
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.
290
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

103
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...
103
Kirchoff's Rules: Application01:22

Kirchoff's Rules: Application

1.5K
Kirchhoff's rules quantify the current flowing through a circuit and the voltage variations around the loop in a circuit. Applying Kirchhoff's rules generates a set of linear equations that allow us to find the unknown values in circuits. These may be currents, voltages, or resistances.
When applying Kirchhoff's first rule, the junction rule, label the current in each branch and decide its direction. If the chosen direction is wrong, it will have the correct magnitude, although the...
1.5K
Reynolds Transport Theorem01:24

Reynolds Transport Theorem

1.2K
The Reynolds transport theorem provides a framework to relate the time rate of change of an extensive property within a system to that in a control volume, which is crucial for analyzing fluid dynamics. Extensive properties, such as mass, velocity, acceleration, temperature, and momentum, can be expressed in terms of the mass of a fluid portion. These properties are called extensive because they depend on the system's size, while intensive properties are their corresponding values per unit...
1.2K
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

3.8K
The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
3.8K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.2K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.2K

You might also read

Related Articles

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

Sort by
Same author

Hierarchy and ranking in fencing and tennis.

Scientific reports·2026
Same author

The desire to avoid cognitive dissonance drives community formation in a social network model.

Scientific reports·2025
Same author

Community detection in hypergraphs through hyperedge percolation.

Scientific reports·2025
Same author

Iterative embedding and reweighting of complex networks reveals community structure.

Scientific reports·2024
Same author

Path of excellence: A co-authorship network analysis of European Research Council grant winners in social sciences.

Heliyon·2024
Same author

Anomalous diffusion analysis of semantic evolution in major Indo-European languages.

PloS one·2024
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: Jul 6, 2025

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

577

Greedy routing optimisation in hyperbolic networks.

Bendegúz Sulyok1, Gergely Palla2,3

  • 1Department of Biological Physics, Eötvös Loránd University, Pázmány P. stny. 1/A, 1117, Budapest, Hungary.

Scientific Reports
|December 28, 2023
PubMed
Summary
This summary is machine-generated.

We developed an optimization method to improve network navigation in hyperbolic embeddings. This approach enhances the success rate of greedy pathfinding, making network navigation more efficient.

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

1.1K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.4K

Related Experiment Videos

Last Updated: Jul 6, 2025

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

577
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

1.1K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.4K

Area of Science:

  • Network science
  • Data visualization
  • Computational geometry

Background:

  • Hyperbolic embeddings are used to represent complex networks in low-dimensional spaces.
  • These embeddings leverage hyperbolic geometry's properties for efficient node distribution and network navigation.
  • Quantifying embedding quality often involves measuring the success rate of navigation protocols.

Purpose of the Study:

  • To develop an optimization scheme for improving network navigability within hyperbolic embeddings.
  • To enhance the success rate of greedy pathfinding algorithms using hyperbolic coordinates.
  • To provide a method that can be used independently or to refine existing hyperbolic embeddings.

Main Methods:

  • An optimization algorithm was developed for hyperbolic embeddings in the native disk representation.
  • The algorithm optimizes the score based on the success rate of greedy paths.
  • The method was tested on both synthetic and real-world network datasets.

Main Results:

  • The proposed optimization scheme significantly improved the success rate of greedy paths in several tested networks.
  • The method enhanced the navigability of existing hyperbolic embeddings.
  • Performance gains were observed for both synthetic and real-world network structures.

Conclusions:

  • The developed optimization technique effectively enhances network navigability in hyperbolic spaces.
  • This method offers a valuable tool for improving the quality of hyperbolic network embeddings.
  • The findings suggest practical applications in network analysis and efficient information retrieval.