A RANDOM EFFECTS STOCHASTIC BLOCK MODEL FOR JOINT COMMUNITY DETECTION IN MULTIPLE NETWORKS WITH APPLICATIONS TO NEUROIMAGING
A RANDOM EFFECTS STOCHASTIC BLOCK MODEL FOR JOINT COMMUNITY DETECTION IN MULTIPLE NETWORKS WITH APPLICATIONS TO NEUROIMAGING
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DOI：
10.1214/20-aoas1339
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发表时间：
2020-06-01
影响因子：
1.8
通讯作者：
Chen, Yuguo
中科院分区：
文献类型：
--
作者：
Paul, Subhadeep;Chen, Yuguo
为了分析神经影像学研究中多学科实验的数据，我们开发了一个建模框架，用于在一组相关网络中进行联合社区检测，这些网络可以被认为是来自网络群体的样本。建议的随机效应随机块模型有利于研究群体差异和特定主题的变化，在社区结构。该模型提出了一个假定的平均社区结构，它代表了所考虑的群体或人口，但不是任何单个组件网络的社区结构。相反，社区成员的节点不同，在每个组件网络的过渡矩阵，从而模拟社区结构的变化在一组科目。为了估计感兴趣的数量，我们提出了两种方法：变分EM算法和无模型的“两步”方法称为Co-OSNTF，它是基于非负矩阵分解。我们还开发了一个基于重采样的假设检验两个种群的社区结构之间的差异，在整个网络水平和节点水平。该方法适用于COBRE数据集，一个公开的fMRI数据集，涉及精神分裂症患者的多学科实验。我们的方法揭示了一个整体的推定的社区结构代表的组，以及特定的主题内的两个组，健康对照组和精神分裂症患者的变化。该模型对训练样本外的同一群体中的个体群体结构具有较好的预测能力。使用我们的网络级假设检验，我们能够确定两组之间社区结构的统计学显著差异，而我们的节点级测试有助于确定驱动差异的节点。
To analyze data from multisubject experiments in neuroimaging studies, we develop a modeling framework for joint community detection in a group of related networks that can be considered as a sample from a population of networks. The proposed random effects stochastic block model facilitates the study of group differences and subject-specific variations in the community structure. The model proposes a putative mean community structure, which is representative of the group or the population under consideration but is not the community structure of any individual component network. Instead, the community memberships of nodes vary in each component network with a transition matrix, thus modeling the variation in community structure across a group of subjects. To estimate the quantities of interest, we propose two methods: a variational EM algorithm and a model-free "two-step" method called Co-OSNTF which is based on nonnegative matrix factorization. We also develop a resampling-based hypothesis test for differences between community structure in two populations both at the whole network level and node level. The methodology is applied to the COBRE dataset, a publicly available fMRI dataset from multisubject experiments involving schizophrenia patients. Our methods reveal an overall putative community structure representative of the group as well as subject-specific variations within each of the two groups, healthy controls and schizophrenia patients. The model has good predictive ability for predicting community structure in subjects from the same population but outside the training sample. Using our network level hypothesis tests, we are able to ascertain statistically significant difference in community structure between the two groups, while our node level tests help determine the nodes that are driving the difference.