通过将度和度量与节点距离集成来增强复杂网络影响节点的检测
Ramya D Shetty1, Rashmi M2, Khyathi Rajesh Shetty3
1Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
Scientific reports
|August 25, 2025
概括
在复杂网络中识别有影响力的节点至关重要. 一种新的度距离组合 (EDDC) 方法有效地结合了本地和全球网络信息,用于流行病建模等应用中准确的节点排名.
科学领域:
- 网络科学
- 计算社会科学
背景情况:
- 复杂的网络对于营销,运输和流行病模型等系统至关重要.
- 识别有影响力的节点是优化流程和防止负面结果的关键.
- 像Degree Centrality和K-shell这样的当前方法在准确性和计算效率上都有局限性.
研究的目的:
- 提出一种新且高效的方法来识别复杂网络中的有影响力的节点.
- 通过整合本地和全球网络特性来克服现有方法的局限性.
- 提高各种现实应用中的影响性节点识别的准确性.
主要方法:
- 开发了度距离组合 (EDDC) 方法.
- 综合局部指标 () 和全球指标 (度,距离,路径信息).
- 通过使用标准评估指标对六个基准数据集进行EDDC方法的评估.
主要成果:
- 在识别有影响力的节点方面,EDDC方法表现出卓越的效率.
- 整合本地和全球措施提高了节点排名的准确性.
- 这种方法在各种网络结构和应用中被证明是有效的.
结论:
- 在影响性节点的识别方面, EDDC 方法提供了显著的进步.
- 这种方法可以更全面地了解网络结构和节点的重要性.
- 在病毒传播建模和病毒营销等领域,EDDC具有很强的应用潜力.
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