在稀疏网络上随机走路的返回时间分布的近似值.
Erik Hormann1, Renaud Lambiotte1, George T Cantwell2
1University of Oxford, Mathematical Institute, Oxford OX2 6GG, United Kingdom.
Physical review. E
|August 1, 2025
概括
我们在网络上开发了一个随机步行第一次返回时间的近似值. 我们的方法准确地预测了分布,这表明网络的全球结构不如局部细节和总边缘重要.
科学领域:
- 网络科学 网络科学
- 统计物理 统计物理
- 图形理论 图形理论
背景情况:
- 随机步行对于分析网络动态至关重要.
- 了解第一回归时间分布对于网络流程至关重要.
- 现有的方法在大规模网络上难以准确近似.
研究的目的:
- 在无定向网络上提出一种新的近似方法,用于第一个随机走路的返回时间分布.
- 为网络分析提供准确的分析工具.
- 调查网络结构对随机步行行为的影响.
主要方法:
- 结合一个消息传递解决方案,用于短期的行为.
- 在长期行为中采用平均场近似.
- 在各种大型图形类上测试近似值.
主要成果:
- 拟议的近似与真实分布之间有很好的一致性.
- 在各种大型图形结构中证明了准确性.
- 量化了本地与全球网络结构的相对重要性.
结论:
- 该近似有效地捕获了第一个回归时间分布.
- 网络的本地结构具有很大的影响力.
- 全球网络结构的影响主要是通过边缘的总数.
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