在复杂网络中通过中间自值统计学探索β-高斯集团的普遍性
Ankit Mishra1, Kang Hao Cheong1,2
1Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, S487372, Singapore.
Physical review. E
|February 17, 2024
概括
在复杂网络中,β-高斯集团准确地描述了近邻固有值统计数据. 然而,它与更高阶的中间固有价值统计扎,只显示质量一致.
科学领域:
- 统计物理 统计物理
- 网络科学 网络科学
- 复杂的系统复杂的系统.
背景情况:
- 固有价值统计对于理解物理系统中的转变至关重要,例如本地化-迁移.
- β-高斯集团是最近的一种单参数模型,用于中间固有值统计.
研究的目的:
- 在复杂网络中研究β-高斯组合的普遍性.
- 将各种网络模型的固有值统计数据与β-高斯集合进行比较.
主要方法:
- 研究了小世界,埃尔多斯-雷尼随机和无尺度网络的固有值统计.
- 与近邻和更高阶中间固有值统计数据与β-高斯组合进行了比较.
主要成果:
- 所有研究网络的近邻固有值统计数据与β-高斯元组一致.
- β-高斯集团在描述高阶中间固有值统计数据 (n≥4) 中表现出局限性.
- β-高斯集团的近邻统计与网络的高阶中间统计相匹配.
结论:
- 在复杂网络中,β-高斯集团对近邻固有值统计具有前景.
- 需要进一步开发β-高斯组合才能准确地捕获更高阶的固有值统计数据.
- 该研究强调了β-高斯集团在网络分析中的潜力和局限性.
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