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

Cluster Sampling Method01:20

Cluster Sampling Method

13.5K
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...
13.5K
Block Diagram Reduction01:22

Block Diagram Reduction

359
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...
359
Propagation of Action Potentials01:23

Propagation of Action Potentials

7.9K
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...
7.9K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

863
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
863
Classification of Systems-II01:31

Classification of Systems-II

297
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
297
Labeling DNA Probes03:31

Labeling DNA Probes

8.7K
DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...
8.7K

You might also read

Related Articles

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

Sort by
Same author

Change in the intestinal bacterial community structure associated with environmental microorganisms during the growth of Eriocheir sinensis.

MicrobiologyOpen·2018
Same author

Transcriptomic analyses identify albino-associated genes of a novel albino tea germplasm 'Huabai 1'.

Horticulture research·2018
Same author

New phenolic halogenated disinfection byproducts in simulated chlorinated drinking water: Identification, decomposition, and control by ozone-activated carbon treatment.

Water research·2018
Same author

Oligonucleotide Aptamer-Mediated Precision Therapy of Hematological Malignancies.

Molecular therapy. Nucleic acids·2018
Same author

Effects of consecutive monoculture of sweet potato on soil bacterial community as determined by pyrosequencing.

Journal of basic microbiology·2018
Same author

Long non-coding RNA OGFRP1 regulates LYPD3 expression by sponging miR-124-3p and promotes non-small cell lung cancer progression.

Biochemical and biophysical research communications·2018

Related Experiment Video

Updated: Nov 7, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.5K

LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection.

Huan Li1, Ruisheng Zhang1, Zhili Zhao1

  • 1School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.

Entropy (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

This study introduces LPA-MNI, an improved label propagation algorithm (LPA) for network community detection. LPA-MNI enhances accuracy and stability by integrating modularity and node importance, outperforming traditional methods.

Keywords:
community detectionlabel propagationmodularitynode importancerandomness

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

1.3K
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

7.1K

Related Experiment Videos

Last Updated: Nov 7, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.5K
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

1.3K
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

7.1K

Area of Science:

  • Network science
  • Data mining
  • Algorithm development

Background:

  • Community detection is crucial for network analysis.
  • Traditional Label Propagation Algorithm (LPA) suffers from randomness and instability.
  • Need for robust and accurate community detection methods.

Purpose of the Study:

  • To propose an improved Label Propagation Algorithm (LPA) named LPA-MNI.
  • To enhance the accuracy and stability of community detection.
  • To address the limitations of the traditional LPA.

Main Methods:

  • LPA-MNI combines modularity function and node importance with the original LPA.
  • Initial communities are identified using modularity values.
  • Label propagation clusters remaining nodes.
  • Node importance refines label updating order and selection mechanisms.

Main Results:

  • LPA-MNI demonstrates superior accuracy and higher modularity compared to six other algorithms.
  • The algorithm yields more reasonable community numbers.
  • Experiments conducted on twelve real-world and eight synthetic networks.
  • LPA-MNI shows improved robustness over traditional LPA.

Conclusions:

  • LPA-MNI is an effective and robust algorithm for network community detection.
  • The integration of modularity and node importance significantly improves LPA's performance.
  • LPA-MNI offers a more stable and accurate approach to understanding network structures.