Abstract Until recently obtaining data on populations of networks was typically rare. However, with the advancement of automatic monitoring devices and the growing social and scientific interest in networks, such data has become more widely available. From sociological experiments involving cognitive social structures to fMRI scans revealing large-scale brain networks of groups of patients, there is a growing awareness that we urgently need tools to analyse populations of networks and particularly to model the variation between networks due to covariates. We propose a model-based clustering method based on mixtures of generalized linear (mixed) models that can be employed to describe the joint distribution of a populations of networks in a parsimonious manner and to identify subpopulations of networks that share certain topological properties of interest (degree distribution, community structure, effect of covariates on the presence of an edge, etc.). Maximum likelihood estimation for the proposed model can be efficiently carried out with an implementation of the EM algorithm. We assess the performance of this method on simulated data and conclude with an example application on advice networks in a small business.

中文翻译：

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网络群体的基于模型的聚类

摘要 直到最近，获得关于网络群体的数据通常很少见。然而，随着自动监测设备的进步以及社会和科学对网络的兴趣日益浓厚，此类数据变得更加广泛可用。从涉及认知社会结构的社会学实验到揭示患者群体大规模大脑网络的 fMRI 扫描，人们越来越意识到我们迫切需要工具来分析网络群体，尤其是对因协变量而导致的网络之间的变化进行建模。我们提出了一种基于广义线性（混合）模型混合的基于模型的聚类方法，该方法可用于以简约的方式描述网络群体的联合分布，并识别共享某些感兴趣的拓扑属性的网络子群体。度分布、社区结构、协变量对边缘存在的影响等）。所提出模型的最大似然估计可以通过 EM 算法的实现有效地进行。我们评估了该方法在模拟数据上的性能，并以在小型企业中的建议网络上的示例应用程序结束。所提出模型的最大似然估计可以通过 EM 算法的实现有效地进行。我们评估了该方法在模拟数据上的性能，并以在小型企业中的建议网络上的示例应用程序结束。所提出模型的最大似然估计可以通过 EM 算法的实现有效地进行。我们评估了该方法在模拟数据上的性能，并以在小型企业中的建议网络上的示例应用程序结束。