Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.7K
Community Based Intervention01:30

Community Based Intervention

66
Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...
66
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
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...
12.0K
Aggregates Classification01:29

Aggregates Classification

350
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...
350
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

426
Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
426
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

170
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
170

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 25, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

585

社区-CL:基于对比学习的增强社区检测算法

Zhaoci Huang1, Wenzhe Xu1, Xinjian Zhuo1

  • 1School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Entropy (Basel, Switzerland)
|June 28, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了社区对比学习 (Community-CL),这是图形表示学习和社区检测的新框架. 社区-CL通过改进节点嵌入和社区结构发现来增强网络分析.

关键词:
社区检测 社区检测相反的学习学习学习.图表神经网络的神经网络

更多相关视频

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

635

相关实验视频

Last Updated: Jul 25, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

585
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

635

科学领域:

  • 图形表示学习学习学习图形表示.
  • 网络分析 网络分析
  • 社区检测检测发现

背景情况:

  • 图形对比学习 (GCL) 是一种强大的自我监督技术,用于图形分析任务,如节点分类和集群.
  • 现有的GCL方法还没有完全探索复杂网络中固有的社区结构.
  • 有需要的方法,共同学习节点表示,并有效地检测社区.

研究的目的:

  • 提出一个新的在线框架,社区对比学习 (Community-CL),用于同时学习节点表示和社区检测.
  • 通过结合社区结构信息来增强图形表示学习.
  • 与传统方法相比,提高网络嵌入的准确性和表现力.

主要方法:

  • 社区-CL使用对比学习来对准不同图形视图中节点和社区的潜在表示.
  • 可学习的图形增强视图是使用图形自动编码器 (GAE) 生成的.
  • 一个共享编码器从原始图形及其增强视图中学习节点特征.

主要成果:

  • 与最先进的基线相比,社区-CL在社区检测方面取得了更好的表现.
  • 该框架在识别网络社区方面表现出更高的准确性.
  • 实验结果显示了显著的性能增长,NMI分数为0.714 (亚马逊-照片) 和0.551 (亚马逊-计算机),代表了高达16%的改进.

结论:

  • 拟议的社区-CL框架有效地整合了代表性学习和社区检测.
  • 联合对比方法导致更准确的网络表示和表达式嵌入.
  • 社区-CL为分析网络的社区结构提供了有希望的进展.