在基于·诺伊曼透的超图中识别重要节点
Feng Hu1,2, Kuo Tian1,2, Zi-Ke Zhang3,4
1School of Computer, Qinghai Normal University, Xining 810008, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了使用·诺伊曼的新型超图重要节点识别方法 (HVC和半SAVC). 这些方法通过利用高阶信息,有效地识别复杂系统中的关键节点,优于现有技术.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 信息理论 信息理论
背景情况:
- 超图在复杂系统中自然模拟高阶合.
- 在超图中使用高阶信息识别重要节点仍然具有挑战性.
- 现有的方法往往无法充分利用复杂的网络结构.
研究的目的:
- 提出在超图中识别重要节点的新方法.
- 使用·诺伊曼来集成高阶网络信息.
- 评估这些方法在识别有影响力和强大的节点方面的有效性.
主要方法:
- 开发了一种基于·诺伊曼的高图重要节点识别方法 (HVC).
- 引入了一种优化的版本 (半SAVC),使用二次近似来提高效率.
- 利用超图的高阶直线图结构来量化节点的重要性.
- 将HVC和半SAVC与基线中心性指标进行比较.
主要成果:
- 高压和半SAVC在识别重要节点方面表现出卓越的表现.
- 这些方法有效地促进了最大的影响力,并保持了网络连接.
- 发现了一种"和效应",在这种情况下,更高层次的信息可能会阻碍识别.
结论:
- 提出的HVC和半SAVC方法对于在超图中识别重要节点是有效的.
- 使用·诺伊曼的高阶信息集成增强了节点重要性量化.
- 和效应强调了平衡信息顺序的重要性,以获得最佳的结果.
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