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在复杂网络中基于证据理论识别有影响力的节点.

Fu Tan1,2, Xiaolong Chen2,3, Rui Chen2

  • 1School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China.

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

本研究引入了一种使用Dempster-Shafer (DS) 证据理论的新方法,用于识别复杂网络中的有影响力的节点. DS方法有效地处理不确定性和多维数据,在网络分解任务中表现优于传统算法.

关键词:
德姆斯特·沙弗证据理论理论复杂的网络复杂的网络.有影响力的节点识别.具有多属性的特征具有多属性的特征.可见度图算法可见度图算法

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科学领域:

  • 复杂网络科学是一个复杂的网络科学.
  • 数据分析数据分析
  • 网络理论 网络理论

背景情况:

  • 在复杂的网络科学中,识别有影响力的节点至关重要.
  • 经典方法与复杂的,高维的现实世界网络作斗争.
  • 现有的方法往往无法充分处理不确定性和相互矛盾的信息.

研究的目的:

  • 提出一种使用Dempster-Shafer (DS) 证据理论进行影响性节点识别的新方法.
  • 提高在复杂网络中影响性节点检测的效率和可靠性.
  • 为了证明该方法在网络解体和财务时间序列分析中的有效性.

主要方法:

  • 拟议的方法利用了Dempster-Shafer (DS) 证据理论.
  • DS理论使用基本信念分配函数量化不确定性.
  • 德普斯特的结合规则用于处理相互矛盾的证据和整合多维信息.

主要成果:

  • 与经典算法相比,DS方法显著改善了有影响力的节点识别.
  • 通过DS方法识别的攻击节点会导致更大的网络解体.
  • 对英期货时间序列的应用揭示了DS方法识别了关键的价格转折点.

结论:

  • 德姆斯特-沙弗 (DS) 证据理论为复杂网络中影响性节点的识别提供了一个强大的框架.
  • 拟议的方法提高了网络分析的可靠性,并提供了对金融市场动态的洞察力.
  • 这种方法有效地处理不确定性和多维数据,优于现有技术.