基于社区结构信息的网络中具有影响力的节点的测量
Xiaohua Wang1, Qing Yang2, Yutao Zhu3
1School of safety science and emergency management, Wuhan University of Technology, Wuhan, 430070, Hubei Province, People's Republic of China.
Scientific reports
|November 29, 2025
概括
我们介绍了Local Community Structure Entropy (LCE),这是一个用于识别复杂网络中具有影响力的节点的新指标. 通过考虑社区结构,LCE提高了准确性,超过了现有的方法.
科学领域:
- 网络科学 网络科学
- 信息理论 信息理论
- 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管理信息流和防止错误信息传播至关重要.
- 目前的方法往往由于对社区结构洞察力的有限使用而失败.
- 这导致了复杂网络中的低于最佳的准确性和概括性.
研究的目的:
- 提出一种新的半局部度量,Local Community Structure Entropy (LCE),用于增强影响性节点的检测.
- 将信息与网络社区架构集成为更强大的指标.
- 为了提高识别准确性和在各种网络数据集中的概括性.
主要方法:
- 使用社区检测算法将网络划分为社区.
- 使用一级和二级邻居信息计算节点的中心性.
- 通过结合连接性和社区结构来开发地方社区结构 (LCE) 度量.
主要成果:
- 在社交和合成网络上进行了广泛的实验,证明了LCE的卓越性能.
- 在多个评估指标中,LCE显著优于七个基准算法.
- 该指标显示了改进的识别能力,效率和普遍性.
结论:
- 地方社区结构 (LCE) 提供了一种更有效的方法来识别有影响力的节点.
- 该方法成功地利用了网络社区结构,以提高准确性.
- LCE为网络分析,信息传播控制和虚假信息缓解提供了有价值的工具.
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