通过简化复合体识别超图中的集团影响,在科学合作中应用
1School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, People's Republic of China.
Chaos (Woodbury, N.Y.)
|August 6, 2025
概括
我们介绍了Hereditary DualRank中心性,这是分析超图形网络的新方法. 这种方法揭示了学者如何根据有效性选择合作,并提供了对研究动态的见解.
科学领域:
- 网络分析 网络分析
- 超图形理论是超图形的理论.
- 复杂的系统复杂的系统.
背景情况:
- 超图对于在网络中建模高阶结构至关重要.
- 现有的网络分析方法经常与复杂的超图结构作斗争.
- 需要强大的中心性措施,适用于各种超图.
研究的目的:
- 为所有超图提出一个无参数的集团中心性指数.
- 引入遗传双排中心性措施.
- 用新的索引分析科学研究中的合作模式.
主要方法:
- 在超图的简化复合体内构建一个遗传类.
- 基于边界-边界关系的内部和外部中心性指数的定义.
- 从全球电路的稳定状态发展出遗传双级中心性.
- 计算简单的有效性和协作效率指数.
主要成果:
- 遗传的DualRank中心性被定义为任何超图中的所有简数.
- 效率指数量化了集团的生产力.
- 对科学合作数据集的分析揭示了学者对有效合作的偏好.
- 协作效率与个人效率的分散有负相关性.
结论:
- 遗传的DualRank中心性提供了对超图的新的拓理解.
- 有效性和效率指数提供了对协作动态的见解.
- 该框架增强了复杂网络演变和决策过程的分析.
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