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

Aggregates Classification01:29

Aggregates Classification

387
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...
387
Classification of Systems-I01:26

Classification of Systems-I

314
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
314
Structural Classification of Joints01:20

Structural Classification of Joints

4.2K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
4.2K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

35.8K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
35.8K
Classification of Systems-II01:31

Classification of Systems-II

242
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,
242
Time-Series Graph00:54

Time-Series Graph

4.5K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.5K

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 author

A novel chimeric peptide binds MC3T3‑E1 cells to titanium and enhances their proliferation and differentiation.

Molecular medicine reports·2013
Same author

Fast trabecular bone strength predictions of HR-pQCT and individual trabeculae segmentation-based plate and rod finite element model discriminate postmenopausal vertebral fractures.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2013
Same author

Biological activities and corresponding SARs of andrographolide and its derivatives.

Mini reviews in medicinal chemistry·2013
Same author

The prognostic value of MGMT promoter methylation in Glioblastoma multiforme: a meta-analysis.

Familial cancer·2013
Same author

Understanding the structure and mechanism of formation of a new magnetic microbubble formulation.

Theranostics·2013
Same author

Analysis of IL-17 gene polymorphisms in Chinese patients with dilated cardiomyopathy.

Human immunology·2013
Same journal

Application of ephrin-B2 loaded glycol chitosan-silk fibroin hydrogel in the treatment of diabetic refractory wounds.

Scientific reports·2026
Same journal

International expert Delphi consensus on thromboprophylaxis in metabolic and bariatric surgery.

Scientific reports·2026
Same journal

Assessing the cross-region knowledge transfer capability of selected deep learning building vectorization methods in the context of available training datasets.

Scientific reports·2026
Same journal

Feasibility and preliminary effects of outdoor versus indoor cognitive-motor therapy in women with Alzheimer's disease: A randomized single-blind pilot study.

Scientific reports·2026
Same journal

Hallmarks of social action in the vocal turn-taking of wild common marmosets (Callithrix jacchus).

Scientific reports·2026
Same journal

Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Sep 15, 2025

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

动态图形结构的演变,用于带有缺失属性的节点分类.

Xiaomeng Song1, Bin Zhou2, Yanjiang Wang3

  • 1School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, People's Republic of China.

Scientific reports
|July 15, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了不断发展的图形结构 (EGS) 框架,以提高图形神经网络 (GNN) 性能,使用不完整的节点数据. EGS可以动态重建节点属性和图形结构,提高半监督节点分类的准确性.

关键词:
缺少的属性 缺少的属性图形神经网络是一个神经网络.节点的分类 节点的分类半监督学习 半监督学习

更多相关视频

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

2.3K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

587

相关实验视频

Last Updated: Sep 15, 2025

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
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

2.3K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

587

科学领域:

  • 机器学习 机器学习
  • 图形神经网络 图形神经网络
  • 数据科学数据科学数据科学

背景情况:

  • 图形神经网络 (GNN) 在许多领域表现出色,但不完整的节点属性阻碍了性能.
  • 现有的图表完成学习 (GCL) 方法依赖于准确的图形结构,这些结构往往有缺陷.
  • 这种限制影响了重建缺失节点属性的可靠性.

研究的目的:

  • 提出进化的图形结构 (EGS) 框架,用于半监督的节点分类,缺少属性.
  • 同时动态重建节点属性和更新图形结构.
  • 为了提高GCL方法的准确性和稳定性.

主要方法:

  • 引入了不断演变的图形结构 (EGS) 框架.
  • 采用了用于动态属性和结构重建的交替优化方法.
  • 使用具有双重约束的迪里克莱特能量函数制定了一个目标函数.

主要成果:

  • 在五个基准数据集上展示了最先进的性能.
  • 在各种缺失数据率中展示了有效性.
  • 验证了七种不同的GNN变体的EGS,优于现有的GCL方法.

结论:

  • EGS框架有效地解决了GNN中不完整的节点属性所带来的挑战.
  • 对属性和图形结构的动态重建对于提高性能至关重要.
  • EGS提供了一个强大的解决方案,用于半监督节点分类在现实世界中场景与不完美的数据.