arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1809.02514math.PRmath.CO
收藏

具有社区的随机交集图

Random intersection graphs with communities

Eindhoven University of Technology(埃因霍温理工大学)

Remco van der Hofstad, Julia Komjathy, Viktoria Vadon

AI总结:

本文提出了一种具有社区结构的随机交集图模型,允许组内存在任意结构,并分析了社区重叠、度分布和局部聚类系数。

摘要:

随机交集图模型具有社区的网络,假设存在组与个体的二分结构,其中这些组可能重叠。组成员资格通过二分配置模型生成。在组成员资格条件下,经典随机交集图通过当个体至少在一个组中共同存在时连接个体获得。我们扩展了这一定义,允许组内存在任意社区结构。在我们的新模型中,组可能重叠,并且它们有自己的内部结构,由图描述,经典情况对应于组为完全图。我们的模型被发现是可处理的。我们分析了社区的重叠结构,推导了渐近度分布和局部聚类系数。这些证明依赖于局部弱收敛,这也意味着子图计数收敛。我们进一步利用与二分配置模型的联系,对于该模型我们也证明了局部弱收敛,其本身也具有趣味性。

英文摘要:

Random intersection graphs model networks with communities, assuming an underlying bipartite structure of groups and individuals, where these groups may overlap. Group memberships are generated through the bipartite configuration model. Conditionally on the group memberships, the classical random intersection graph is obtained by connecting individuals when they are together in at least one group. We generalize this definition, allowing for arbitrary community structures within the groups. In our new model, groups might overlap and they have their own internal structure described by a graph, the classical setting corresponding to groups being complete graphs. Our model turns out to be tractable. We analyze the overlapping structure of the communities, derive the asymptotic degree distribution and the local clustering coefficient. These proofs rely on local weak convergence, which also implies that subgraph counts converge. We further exploit the connection to the bipartite configuration model, for which we also prove local weak convergence, and which is interesting in its own right.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑