社区结构的稳固性在边缘添加下添加
1Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912, USA.
Physical review. E
|December 20, 2023
概括
这项研究探讨了增加连接如何影响网络社区. 社区对随机添加和更强的初始结构更具稳定性,根据检测方法的稳定性有所不同.
科学领域:
- 网络科学 网络科学
- 复杂系统分析 复杂系统分析
- 数据挖掘和社区检测
背景情况:
- 社区是基本的网络结构.
- 了解社区对干扰的强度至关重要.
- 边缘添加对社区强度的影响还未得到充分研究.
研究的目的:
- 分析边缘添加对社区强度的影响.
- 调查随机与有针对性的边缘加值场景.
- 在不同的社区检测算法中评估稳定性.
主要方法:
- 应用边缘添加到合成和实证时间网络.
- 使用了Infomap,标签传播,莱登和卢温算法.
- 使用相似度指标评估社区结构的变化.
主要成果:
- 社区的稳定性更高,具有更强的初始分区和随机边缘添加.
- 经验数据显示,社区相似度指标影响了强度.
- 不同的社区检测算法产生不同的稳定性水平.
结论:
- 网络社区的稳定性对边缘添加策略很敏感.
- 社区检测算法的选择显著影响观察到的稳定性.
- 需要进一步的研究来了解动态网络中的边缘加值效应.
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