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

Propagation of Action Potentials01:23

Propagation of Action Potentials

8.8K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
8.8K
Aggregates Classification01:29

Aggregates Classification

963
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
963
Cluster Sampling Method01:20

Cluster Sampling Method

14.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.0K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K
Propagation of Waves01:07

Propagation of Waves

2.8K
When a wave propagates from one medium to another, part of it may get reflected in the first medium, and part of it may get transmitted to the second medium. In such a case, the interface of the two mediums can be considered as a boundary that is neither fixed nor free.
Consider a scenario where a wave propagates from a string of low linear mass density to a string of high linear mass density. In such a case, the reflected wave is out of phase with respect to the incident wave, however the...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Development and characterization of potato starch/sodium alginate active composite films incorporated with theabrownin and their application in fresh Auricularia auricula-judae preservation.

International journal of biological macromolecules·2026
Same author

Intestinal neutral ceramidase exacerbates MASH pathogenesis.

eGastroenterology·2026
Same author

Correction: Robot-assisted scaphoid screw fixation versus free-hand technique for scaphoid fractures: a systematic review and meta-analysis.

Journal of robotic surgery·2026
Same author

Charge density wave in a band insulator.

Nature communications·2026
Same author

Granitic intrusions enhance strain localization and rapid mantle exhumation along an oceanic detachment fault.

Science advances·2026
Same author

Deulorlatinib (TGRX-326) in ALK Gene Fusion Positive NSCLC After Failure of Second-Generation Inhibitors: A Single-Arm, Multicenter, Phase 2 Trial.

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2026

Related Experiment Videos

A Community Detection Model Based on Dynamic Propagation-Aware Multi-Hop Feature Aggregation.

Chao Lei1,2, Yuzhi Xiao1,2, Sheng Jin1,2

  • 1School of Computer Science, Qinghai Normal University, Xining 810008, China.

Entropy (Basel, Switzerland)
|October 28, 2025
PubMed
Summary

We introduce DAMA, a novel community detection model. DAMA effectively captures dynamic information flow and network structures, outperforming existing methods on real-world and synthetic networks.

Keywords:
adaptive graph samplingcommunity detectiondynamic propagation modelinggated mechanismmulti-hop feature aggregation

Related Experiment Videos

Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Community detection is vital for understanding network structures and information flow.
  • Current methods often overlook dynamic propagation patterns and nonlinear attenuation in static networks.

Purpose of the Study:

  • To propose DAMA, a community detection model addressing limitations of static approaches.
  • To integrate dynamic propagation features and adaptive multi-hop structural aggregation.

Main Methods:

  • Constructing an Information Flow Matrix (IFM) to model nonlinear information attenuation.
  • Employing an Adaptive Sparse Sampling Module for neighbor selection and denoising.
  • Utilizing a Hierarchical Multi-Hop Aggregation Framework with a dual-gating mechanism.

Main Results:

  • DAMA effectively enriches static network representations with dynamic propagation dynamics.
  • The model improves structural denoising and preserves essential diffusion pathways.
  • DAMA demonstrates superior performance in community detection tasks on diverse networks.

Conclusions:

  • DAMA offers a significant advancement in community detection by incorporating dynamic propagation information.
  • The model's adaptive aggregation framework enhances the expressiveness of structural embeddings.
  • DAMA provides a more robust and accurate approach for analyzing complex network structures.