随机的超标图形与任意的中等尺度结构
Stefano Guarino1, Enrico Mastrostefano1, Davide Torre2
1Istituto per le Applicazioni del Calcolo "Mauro Picone" (CNR-IAC), Via dei Taurini 19, Rome 00185, Italy.
Physical review. E
|December 23, 2025
概括
我们介绍了随机超标块模型 (RHBM),以更好地捕捉真实世界网络中的社区结构. 这种模型通过结合块结构来扩展随机的超标图形,克服纯几何方法的局限性.
科学领域:
- 网络科学 网络科学
- 复杂的系统复杂的系统.
- 图形理论 图形理论
背景情况:
- 现实世界的网络表现出普遍性质,如稀疏性,小世界性和社区结构.
- 几何网络模型,如随机过度图 (RHGs),通过嵌入节点在潜在相似空间中来捕捉许多网络特征.
- 然而,纯几何模型很难代表非几何社区结构,其中群体间的不相似性违反了三角形不平等.
研究的目的:
- 解决现有的几何网络模型在捕捉中大尺度社区结构方面的局限性.
- 引入一种新的网络模型,它结合了区块结构,同时保留了隐性几何.
- 增强网络模型能够代表各种现实世界网络拓学的能力.
主要方法:
- 介绍随机超标块模型 (RHBM),用块结构扩展RHG.
- 使用最大的框架将社区结构纳入模型.
- 合成网络的分析,以证明RHBM的功能.
主要成果:
- 在这个方面,RHBM有效地保留了社区结构,在这个方面表现优于纯粹的几何模型.
- 该模型展示了在产生具有特定中等尺度混合模式的网络方面的灵活性.
- 合成网络分析验证了RHBM在捕获非几何社区特征方面的优势.
结论:
- 随机超标块模型 (RHBM) 在模拟具有社区结构的复杂网络方面取得了重大进展.
- RHBM强调了隐性几何学的重要性,同时解决了它在控制大尺度组织方面的局限性.
- 这个模型提供了一个更准确的真实世界网络的表现,其特点是几何和块属性.
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