Testing for Equivalence of Network Distribution Using Subgraph Counts
提出一种统计方法，通过分析多个观测网络的子图计数联合渐近性质，检验这些网络是否来自指定模型或分布，并应用于脑网络分析发现高创造力者大脑有更多短环。
We consider that a network is an observation, and a collection of observed networks forms a sample. In this setting, we provide methods to test whether all observations in a network sample are drawn from a specified model. We achieve this by deriving the joint asymptotic properties of average subgraph counts as the number of observed networks increases but the number of nodes in each network remains finite. In doing so, we do not require that each observed network contains the same number of nodes, or is drawn from the same distribution. Our results yield joint confidence regions for subgraph counts, and therefore methods for testing whether the observations in a network sample are drawn from: a specified distribution, a specified model, or from the same model as another network sample. We present simulation experiments and an illustrative example on a sample of brain networks where we find that highly creative individuals’ brains present significantly more short cycles than found in less creative people. Supplementary materials for this article are available online.