影响性节点识别方法基于多顺序邻居和专属邻居的方法
Feifei Wang1, Zejun Sun1, Guan Wang1
1School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, China.
PloS one
|August 13, 2025
概括
我们引入了一种新方法,多级邻居和专属邻居 (MNEN),以识别复杂网络中的关键节点. MNEN准确地确定有影响力的节点,以便更好地进行网络分析和控制.
科学领域:
- 网络科学 网络科学
- 复杂系统分析 复杂系统分析
- 计算社会科学 计算社会科学
背景情况:
- 识别有影响力的节点对于理解复杂的网络结构至关重要.
- 对于节点影响识别的现有方法在准确性和范围上有局限性.
- 关键节点对于信息传播,资源分配和网络管理至关重要.
研究的目的:
- 提出一种用于识别复杂网络中具有影响力的节点的新方法.
- 为了提高关键节点本地化的准确性和普遍性.
- 为网络分析和控制提供一个强大的工具.
主要方法:
- 开发了多级邻居和专属邻居 (MNEN) 方法.
- 使用度和K-shell (Ks) 值计算节点影响.
- 纳入多顺序邻居贡献和独家邻居分析.
主要成果:
- 在识别有影响力的节点方面,MNEN方法表现出了卓越的性能.
- 使用SIR模型的模拟实验验证了算法的有效性.
- 在不同规模的网络中,MNEN显示了准确性,表明了普遍性.
结论:
- 在识别有影响力的节点方面,MNEN方法提供了显著的进步.
- 这种方法为网络监管和信息控制提供了可靠的工具.
- 在各种复杂的网络中,MNEN表现出广泛的适用性和有效性.
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