时间局部聚类系数揭示了时间网络中隐藏的模式.
Bofan Chen1,2, Guyu Hou1,3, Aming Li1,4
1Center for Systems and Control, College of Engineering, <a href="https://ror.org/02v51f717">Peking University</a>, Beijing 100871, People's Republic of China.
Physical review. E
|July 18, 2024
概括
本研究介绍了用于分析动态复杂网络的时间局部聚类系数 (TC). TC揭示了时间网络中不同的交互模式,与静态网络分析不同.
科学领域:
- 复杂网络理论 复杂网络理论
- 网络科学 网络科学
- 数据分析数据分析
背景情况:
- 了解复杂网络依赖于拓特征.
- 时间网络需要超越静态模型的分析,包括时间变化的交互.
- 像集群系数 (C) 这样的经典指标对于时间动态来说是不够的.
研究的目的:
- 将局部聚类系数分析扩展到时间网络.
- 引入和分析时间局部聚类系数 (TC).
- 在时间变化的交互过程中发现节点连接中的隐藏信息.
主要方法:
- 为静态网络扩展传统的局部集群系数 (C).
- 为时间网络开发和应用时间局部聚类系数 (TC).
- 对各种实证数据集的系统分析.
主要成果:
- 时间局部集群系数 (TC) 捕获有关节点连接节奏的信息.
- 在各种时间网络类型中,TC揭示了不同的交互模式.
- 在效率网络中,TC与C有很强的相关性,但在社会活动网络中却没有.
- TC区分了实际的集群特性和偶然的相互作用.
结论:
- 时间局部聚类系数 (TC) 对于理解时间网络结构至关重要.
- 动态特征对于完全理解真实复杂系统至关重要.
- TC提供了与静态分析不同的网络行为洞察力.
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