论文标题
网络集合的指数随机图模型的贝叶斯非参数混合物
Bayesian Nonparametric Mixtures of Exponential Random Graph Models for Ensembles of Networks
论文作者
论文摘要
网络的集合在各个领域都出现，其中在同一节点上观察到多个独立网络，例如，在不同个体的同一大脑区域上构建的大脑网络集合。但是，很少有模型同时描述集合的网络的变化和特征。在本文中，我们建议使用指数随机图模型（DPM-ergerm）的Dirichlet过程混合物对网络的集合进行建模，该过程将集合分为不同的群集，并使用单独的指数随机图模型（ERGM）将集合分为不同的群集和模型。通过使用Dirichlet过程混合物，可以自动确定簇的数量，并根据提供的数据自适应地更改。此外，为了对DPM-ergm进行完整的贝叶斯推断，我们在Metropolis-within-slice采样方案中采用了中间的重要性抽样技术，这解决了在无限采样空间上从棘手的ERGM中采样的问题。我们还通过模拟数据集和真实数据集证明了DPM-gergm的性能。
Ensembles of networks arise in various fields where multiple independent networks are observed on the same set of nodes, for example, a collection of brain networks constructed on the same brain regions for different individuals. However, there are few models that describe both the variations and characteristics of networks in an ensemble at the same time. In this paper, we propose to model the ensemble of networks using a Dirichlet Process Mixture of Exponential Random Graph Models (DPM-ERGMs), which divides the ensemble into different clusters and models each cluster of networks using a separate Exponential Random Graph Model (ERGM). By employing a Dirichlet process mixture, the number of clusters can be determined automatically and changed adaptively with the data provided. Moreover, in order to perform full Bayesian inference for DPM-ERGMs, we employ the intermediate importance sampling technique inside the Metropolis-within-slice sampling scheme, which addressed the problem of sampling from the intractable ERGMs on an infinite sample space. We also demonstrate the performance of DPM-ERGMs with both simulated and real datasets.
