在简单复合体上具有更高阶的同类性.
Arnab Sarker1, Natalie Northrup2, Ali Jadbabaie1,2
1Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139.
概括
高级网络分析需要新的同类性测量. 我们引入了简化的同类性,以准确评估群体相互作用,揭示复杂系统中隐藏的模式.
科学领域:
- 网络科学 网络科学
- 社会学 社会学 社会学
- 数据分析 数据分析
背景情况:
- 高级网络模型捕捉多实体交互,对复杂系统至关重要.
- 同性恋,类似实体连接的倾向,在对联网络中得到了很好的研究.
- 现有的同类性测量与更高层次的相互作用作斗争,经常掩盖真实模式.
研究的目的:
- 为更高层次网络开发一种新型的同类性测量方法.
- 为了将双向同性恋与更高阶同性恋脱而出.
- 调查同类关系在高阶链接预测中的作用.
主要方法:
- 定义了作为简单复合体建模的网络的简单化同类关系.
- 理论上验证了测量方法,使用对简化复合体的随机区块模型.
- 在16个大规模数据集上经验测试了该措施.
主要成果:
- 标准的同类性测量因对效应而导致更高阶的结果膨胀.
- 简单的同性恋精确地区分了对对与更高阶的同性恋.
- 该测量可以识别何时节点特征在更高阶网络中具有预测性.
结论:
- 简化的同类性提供了对复杂系统中群体动态的更准确的理解.
- 高级网络中节点特征的重要性是微妙的,取决于交互顺序.
- 这项工作提供了一种精细的方法来分析高阶网络结构和属性.
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