使用配置模型和最小描述长度的网络压缩
Laurent Hébert-Dufresne1,2, Jean-Gabriel Young1,2,3,4, Alexander Daniels1
1Vermont Complex Systems Center, <a href="https://ror.org/0155zta11">University of Vermont</a>, Burlington, Vermont 05405, USA.
Physical review. E
|October 19, 2024
概括
配置模型是网络科学的基本工具. 这项研究发现,对于平均度超过10的网络,经典的配置模型是最好的,而稀疏的网络则受益于分层配置模型.
科学领域:
- 网络科学 网络科学
- 统计建模 统计建模
- 数据分析 数据分析
背景情况:
- 随机网络模型对于分析网络数据至关重要.
- 配置模型,它通过度分布来限制网络,被广泛使用,但往往没有统计验证而被选择.
- 评估网络模型质量需要评估信息要求和生成准确性.
研究的目的:
- 评估和比较网络表示的不同配置模型变体.
- 应用最小描述长度原则来统计选择最佳模型.
- 为了确定哪个配置模型最能代表多样化的现实世界网络.
主要方法:
- 计算了各种配置模型的网络集群的大小,包括那些具有度相关性和中心层的模型.
- 应用了最小描述长度原则作为模型选择标准.
- 分析了来自不同领域的100多个网络的数据集.
主要成果:
- 对于平均度大于10的网络,更喜欢使用经典的配置模型.
- 一个分层的配置模型,结合中心度指标,为大多数稀疏网络提供最紧的表示.
- 模型选择根据网络特征,如平均程度和稀疏性而有所不同.
结论:
- 配置模型的选择显著影响网络表示质量.
- 使用最小描述长度等原则的统计模型选择对于准确的网络分析至关重要.
- 不同的网络结构需要不同的建模方法来实现最佳表示.
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