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相关概念视频

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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.
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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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相关实验视频

Updated: Jun 3, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Published on: October 13, 2023

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在复杂网络中识别关键节点的多属性决策方法.

Xinyun Zhao1, Yongheng Zhang1, Qingying Zhai2

  • 1Electronic Engineering Institute, National University of Defense Technology, Hefei 230037, China.

Entropy (Basel, Switzerland)
|January 8, 2025
PubMed
概括

识别有影响力的节点是网络安全的关键. 一个新的多属性指标MCTNDI提供了比单属性方法更全面的方法,改善了复杂网络中关键节点的识别.

关键词:
复杂的网络复杂的网络.关键节点识别 关键节点识别多个属性决策的决策.节点重要性指标 节点重要性指标

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科学领域:

  • 网络科学 网络科学
  • 复杂系统分析 复杂系统分析
  • 信息安全 信息安全

背景情况:

  • 识别有影响力的节点对于网络安全和有针对性的保护至关重要.
  • 现有的中心性指标 (程度,接近程度,间距,H指数,K) 提供了对节点重要性的有限视角.
  • 目前还没有全面评估复杂网络中节点的重要性.

研究的目的:

  • 提出一种新的多属性指标,即多属性CRITIC-TOPSIS网络决策指标 (MCTNDI),用于识别关键节点.
  • 通过整合多个网络视角来克服单一属性指标的局限性.
  • 为了提供一个更准确和更全面的衡量节点在复杂网络中的重要性.

主要方法:

  • 接近中心性,中间中心性,H指数和网络约束系数的整合.
  • 开发了多属性的CRITIC-TOPSIS网络决策指标 (MCTNDI).
  • 使用现实世界的网络数据集进行验证 (邻近的美国,海豚,USAir97,Tech-routers-rf).

主要成果:

  • 通过结合本地邻里重要性,拓位置,路径中心性和节点相互信息,MCTNDI有效地识别了关键节点.
  • 与现有方法相比,验证证明了MCTNDI在模拟网络攻击和节点重要性分布方面的优越性能.
  • 对排名单调性和指标相似性的分析证实了MCTNDI的稳定性和全面性.

结论:

  • 拟议的MCTNDI提供了在复杂网络中节点重要性更全面,更准确的评估.
  • MCTNDI解决了传统指标的片面性,增强了网络安全分析.
  • 该指标的有效性在各种现实世界网络中得到验证,这表明它具有广泛的适用性.