基于局部结构的复杂网络中识别有影响力的节点的新型投票措施
Haoyang Li1, Xing Wang1, You Chen1
1Air Force Engineering University, Xi'an, 710038, Shaanxi, China.
Scientific reports
|January 11, 2025
概括
一个新的边缘加权投票等级 (EWV) 算法改善了网络中具有影响力的节点识别. EWV考虑了节点属性和社区结构,在准确性和有效性方面超过了现有的方法.
科学领域:
- 网络科学 网络科学
- 图形理论 图形理论
- 数据挖掘 数据挖掘
背景情况:
- 识别有影响力的节点对于理解网络结构和功能至关重要.
- 由于对网络拓学的考虑不足,像VoteRank这样的现有算法缺乏准确性.
- 需要改进的方法,包括节点属性和邻里信息.
研究的目的:
- 提出一个增强的算法,边缘加权投票等级 (EWV),以更准确地识别有影响力的节点.
- 利用节点属性和社区结构来改进现有方法.
- 为了解决以前算法的局限性,例如精度差和单调性.
主要方法:
- 开发了以人类投票行为为灵感的边缘加权投票排名 (EWV) 算法.
- 嵌入边缘权重来表示节点对其一级邻近的吸引力.
- 在投票过程中引入了节点相似性,并降低了二级社区的投票能力,以防止集群.
主要成果:
- 与12个现实世界网络中的7个其他算法相比,EWV表现出卓越的性能.
- 该算法显示了增强的节点区分能力和排名列表准确性.
- 经验结果验证了EWV在识别有影响力的节点方面的有效性.
结论:
- 边缘加权投票排名 (EWV) 算法为在复杂网络中识别有影响力的节点提供了显著的改进.
- EWV的结合边缘权重和节点相似性的方法提高了准确性和有效性.
- 这种方法为网络分析和理解提供了更强大的工具.
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