社区信息的重叠社区和,用于识别复杂网络中的有影响力的节点
1Shanxi Police College, Shanxi Taiyuan, 030401, China. amber202412@163.com.
Scientific reports
|November 29, 2025
概括
一种新的方法,重叠社区和邻居基于的影响 (OCNEI),识别了复杂网络中的关键节点. 通过分析社区结构和节点连接,OCNEI改善了影响力检测,优于现有的方法.
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流行病控制和信息传播等领域的网络动态至关重要.
- 使用节点度或中心性指数的传统方法往往无法捕捉复杂的网络结构和本地/全球影响.
- 现有的方法可能会忽视网络异质性和节点在重叠社区中扮演的细微角色.
研究的目的:
- 提出一种新的方法,即基于重叠社区和邻里的影响力 (OCNEI),以更准确地检测有影响力的节点.
- 将重叠的社区结构与社区信息融合为全面的影响评估.
- 开发一种可扩展和精确的方法,平衡本地和全球网络属性.
主要方法:
- 利用重叠社区检测来表示异质网络关系,允许节点属于多个社区.
- 在社区背景下计算社区信息,以测量本地连接多样性和不确定性.
- 通过结合邻近节点贡献和全球距离评估来进行强有力的评估,增强了影响度量.
主要成果:
- 与现有方法相比,OCNEI方法在识别有影响力的节点方面表现优越.
- 在合成和现实世界网络上的模拟显示了OCNEI的有效性.
- 在SIR模型下,在影响检测准确度方面取得了2.7%的改进.
结论:
- 在复杂网络中,OCNEI提供了一种更可靠,更准确的方法来识别有影响力的节点.
- 重叠社区和邻里的整合提供了对节点影响的细微理解.
- OCNEI为网络科学应用提供了一个可扩展和精确的解决方案,其性能优于传统方法.
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