论文标题
与高阶结构中的图形混合图案
Mixing patterns in graphs with higher-order structure
论文作者
论文摘要
在本文中，我们研究了具有非平凡聚类和基于子图的分类混合的高阶网络的渗透特性（基于子图联合度的顶点连接到其他顶点的趋势）。我们的分析方法基于生成功能。我们还提出了一种蒙特卡洛图生成算法，以从具有固定统计的图的集合中绘制随机网络。我们使用我们的模型来了解网络微观结构通过聚类对全局属性的影响。最后，我们使用边缘分离集团使用我们的配方来表示经验网络，发现所得模型比基于边缘的理论有了显着改进。
In this paper we examine the percolation properties of higher-order networks that have non-trivial clustering and subgraph-based assortative mixing (the tendency of vertices to connect to other vertices based on subgraph joint degree). Our analytical method is based on generating functions. We also propose a Monte Carlo graph generation algorithm to draw random networks from the ensemble of graphs with fixed statistics. We use our model to understand the effect that network microstructure has, through the arrangement of clustering, on the global properties. Finally, we use an edge disjoint clique cover to represent empirical networks using our formulation, finding the resultant model offers a significant improvement over edge-based theory.
