Jove
Visualize
联系我们

相关概念视频

Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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

Antibacterial mechanisms of magnolol against Streptococcus agalactiae and immunomodulatory effects in Schizothorax prenanti.

Fish & shellfish immunology·2026
Same author

Pressure-Induced Crossover from Antiferromagnetism to Single-Band Superconductivity via Multiband Superconductivity in Quasi-1D CrZr<sub>4</sub>Te<sub>14</sub>.

Journal of the American Chemical Society·2026
Same author

Flooded cultivation improves grain yield and appearance quality while reducing nutritional quality in Shanlan upland rice (Oryza sativa L.).

Food chemistry·2026
Same author

Mechanistic dissection of SMARCA2/4 molecular glues reveals programmable switching between DCAF16 and FBXO22.

Cell chemical biology·2026
Same author

Discovery of Serum Exosomal Protein Biomarkers for Early- and Late-Stage Lung Cancer Through Comparative Proteomic Analysis.

Anti-cancer agents in medicinal chemistry·2026
Same author

The effects of hypoxia environment at high-altitudes on inflammation and angiogenesis in wound healing process:A meta-analysis and review.

Journal of tissue viability·2026
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

相关实验视频

Updated: Jan 16, 2026

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

2.6K

基于碎形维度和力驱动的弹模型的复杂网络中的增强关键节点识别.

Zhaoliang Zhou1, Xiaoli Huang1,2, Zhaoyan Li3

  • 1School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
概括

在复杂网络中识别关键节点是具有挑战性的. 第二阶邻域透模糊局部维度弹模型 (SNEFLD-SM) 通过整合多个中心性测量和信息来提高关键节点检测的准确性.

关键词:
减弱因子是一种减弱因子.复杂的网络复杂的网络.分形技术的技术.确定关键节点的关键节点.信息是信息的.节点影响范围范围 节点影响范围春季模型 春季模型 春季模型

更多相关视频

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

1.5K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.6K

相关实验视频

Last Updated: Jan 16, 2026

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

2.6K
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

1.5K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.6K

科学领域:

  • 网络科学 网络科学
  • 复杂系统分析 复杂系统分析
  • 计算图形理论 计算图形理论

背景情况:

  • 识别关键节点对于理解和管理复杂网络至关重要.
  • 传统的中心性方法通常依赖于有限的本地或全球网络信息.
  • 现有的方法可能会与多个规模的网络和"富人俱乐部"现象作斗争.

研究的目的:

  • 提出一种用于精确识别复杂网络中关键节点的新型模型.
  • 通过结合各种网络属性来克服传统中心性方法的局限性.
  • 为了提高关键节点检测的稳定性和效率.

主要方法:

  • 开发了第二阶邻域透模糊局部维度弹模型 (SNEFLD-SM).
  • 在一个弹模型框架内,整合了二级社区中心性,中间中心性和碎形维度.
  • 集成的信息和节点影响范围,具有减弱因子来减轻"富人俱乐部"效应.

主要成果:

  • 与六个测试网络中的传统方法相比,SNEFLD-SM在关键节点检测中表现出更高的准确性.
  • 该模型有效地捕捉了使用碎形技术的网络自我相似性和层次结构.
  • 信息增强了模型区分节点重要性的能力,并降低了计算成本.

结论:

  • SNEFLD-SM提供了一种更准确,更全面的方法来识别复杂网络中的关键节点.
  • 分形维度和信息的整合提供了对多尺度网络属性的优越分析.
  • 该模型抑制"富人俱乐部"现象的能力提高了它对各种网络结构的适用性.