Testing for Equivalence of Network Distribution Using Subgraph Counts
Testing for Equivalence of Network Distribution Using Subgraph Counts
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DOI：
10.1080/10618600.2020.1736085
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发表时间：
2017-01
影响因子：
2.4
通讯作者：
P. Maugis;C. Priebe;S. Olhede;P. Wolfe
中科院分区：
文献类型：
--
作者：
P. Maugis;C. Priebe;S. Olhede;P. Wolfe
摘要我们认为网络是一个观察，观察到的网络的集合形成一个样本。在此设置中，我们提供了一些方法来测试网络样本中的所有观测是否都来自指定的模型。我们通过推导平均子图计数的联合渐近性质来实现这一点，因为观察到的网络的数量增加，但每个网络中的节点数仍然有限。在这样做时，我们不要求每个观察到的网络包含相同数量的节点，或者来自相同的分布。我们的研究结果产生联合置信区域的子图计数，因此方法测试是否在网络样本中的观察是从：一个指定的分布，一个指定的模型，或从同一个模型作为另一个网络样本。我们提出了模拟实验和一个说明性的例子，在一个样本的大脑网络，我们发现，高度创造性的个人的大脑呈现显着更短的周期比发现在创造力较低的人。本文的补充材料可在网上查阅。
Abstract 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.