在更高层次的复杂网络中排名集团
Yang Zhao1, Cong Li1, Dinghua Shi2
1Adaptive Networks and Control Lab, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
Chaos (Woodbury, N.Y.)
|July 18, 2023
概括
本研究引入了高阶网络分析,以更好地理解复杂的系统,而不仅仅是简单的对联链接. 像高阶PageRank (HOP) 这样的新指标可以识别用于动态和控制的关键网络结构.
科学领域:
- 网络科学 网络科学
- 复杂系统分析 复杂系统分析
- 图形理论 图形理论
背景情况:
- 传统的网络分析主要是对对互动的模型,忽视了更高阶结构.
- 高阶结构显著影响网络动态,功能和新出现的现象,如同步和流行病传播.
- 了解这些高阶相互作用对于分析大规模复杂网络至关重要.
研究的目的:
- 提出新的更高层次的中心性措施,以量化和排序集团在复杂网络中的重要性.
- 评估这些新指标在识别重要节点和理解网络行为的有效性.
- 在网络同步和流行病控制等应用中展示更高阶分析的实用性.
主要方法:
- 高阶中心性指标的发展:高阶周期 (HOC) 比率,高阶程度,高阶H指数和高阶PageRank (HOP).
- 应用这些指标来分析合成和现实世界的网络数据集.
- 高级指标与传统网络分析指标的比较.
主要成果:
- 拟议的更高层次的中心性有效地减少了网络的维度,并提高了识别重要节点的准确性.
- 高级PageRank (HOP) 和高级周期 (HOC) 的指标在排名关键小组中优于传统指标.
- 通过HOP和HOC排名的集团对于保持网络连接,促进同步和控制病毒传播至关重要.
结论:
- 高阶网络分析提供了比传统的双向方法更全面的理解复杂系统.
- 开发的更高层次的中心性指标,特别是HOP和HOC,是识别网络中有影响力的结构的有效工具.
- 这些发现对网络动态,同步和疾病控制策略有重大影响.
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