AI 中文总结
研究随机图顶点度相关性，定义联合度相关函数研究子图组织，找到临界点平均联合度表达式，还引入边不相交团簇分解算法研究经验网络子图相关性。
AI 中文摘要
当存在聚类时，随机图中顶点的度之间通常会出现相关性。在本文中，我们为包含团簇子图的聚类配置模型网络的巨型组件中的顶点定义了一个联合度相关函数。我们使用这个模型详细研究了随机图中最近邻子图之间的组织，它是子图拓扑和聚类的函数。我们找到了这些网络在临界点时巨型组件中邻居的平均联合度的表达式。最后，我们引入了一种新颖的边不相交团簇分解算法，并研究了经验网络子图之间的相关性。
英文摘要
Correlations among the degrees of vertices in random graphs often occur when clustering is present. In this paper we define a joint-degree correlation function for vertices in the giant component of clustered configuration model networks which are composed of clique subgraphs. We use this model to investigate, in detail, the organization among nearest-neighbor subgraphs for random graphs as a function of subgraph topology as well as clustering. We find an expression for the average joint degree of a neighbor in the giant component at the critical point for these networks. Finally, we introduce a novel edge-disjoint clique decomposition algorithm and investigate the correlations between the subgraphs of empirical networks.
Comments17 pages, 9 figures
Journal refPhys. Rev. E 105, 044314 - Published 20 April, 2022
DOI：10.1103/PhysRevE.105.044314
