通过三元闭合强烈聚类的随机图:一个完全可解决的模型模型
Lorenzo Cirigliano1,2, Claudio Castellano2,3, Gareth J Baxter4
1Dipartimento di Fisica Università "Sapienza", P. le A. Moro, 2, I-00185 Rome, Italy.
Physical review. E
|March 16, 2024
概括
本研究介绍了对集群网络的静态三元关闭 (STC) 模型. 该模型通过分析三元组形成概率和骨干网络属性,准确地预测网络集群.
科学领域:
- 网络科学 网络科学
- 统计物理 统计物理
- 复杂的系统复杂的系统.
背景情况:
- 三元关闭是驱动网络集群在现实世界系统中的关键机制.
- 了解模式形成对于描述网络拓学至关重要.
研究的目的:
- 引入和分析静态三元闭合 (STC) 模型,用于生成集群网络.
- 根据骨干网络属性来导出小型网络模式的分析表达式.
- 调查网络异质性对过渡性和更高阶动机的影响.
主要方法:
- 定义一个静态三元关闭 (STC) 模型.
- 准确的表达式的导出预期的动机计数使用时刻的骨干度分布.
- 基于网络异质性的图案密度过渡的分析.
- 在现实世界网络数据集上测试图形密度之间的近似关系.
主要成果:
- 准确的表达式为预期的三角形数量,4-循环,钻石,和4-cliques衍生.
- 证明过渡性和一般化动机对骨干网络异质性的依赖.
- 由于拓学上不等价的三位数,在网络结构中确定了过渡.
- 与现实世界网络数据对比,对图案密度之间的近似关系进行了验证.
结论:
- 静态三元关闭 (STC) 模型为生成集群网络提供了一个现实的和分析可处理的机制.
- 网络异质性对网络模式的形成和整体过渡性产生重大影响.
- STC模型为基础复杂网络形成的基本过程提供了有价值的见解.
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