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)
|April 26, 2025
概括
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.
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.
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