Recovering shared structure from multiple networks with unknown edge distributions
Recovering shared structure from multiple networks with unknown edge distributions
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发表时间：
2019-06
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通讯作者：
Keith D. Levin;A. Lodhia;E. Levina
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作者：
Keith D. Levin;A. Lodhia;E. Levina
在越来越多的情况下，数据集由来自网络群体的多个样本组成，顶点在这些网络上对齐。例如，神经科学中的大脑连接网络由大脑区域之间的相互作用组成，这些区域已经对齐到一个共同的模板。我们考虑观察到的网络有一个共同的期望，但在其边缘的噪声结构可能不同的设置。我们的方法利用共享均值结构对观察到的网络的边缘水平测量进行降噪，并估计潜在的总体水平参数。我们还探讨了边缘水平误差影响估计和下游推理的程度。我们建立了一个有限样本浓度不等式的低秩特征值截断随机加权邻接矩阵，可能是独立的兴趣。提出的方法是在合成网络和数据从精神分裂症的功能磁共振成像研究说明。
In increasingly many settings, data sets consist of multiple samples from a population of networks, with vertices aligned across these networks. For example, brain connectivity networks in neuroscience consist of measures of interaction between brain regions that have been aligned to a common template. We consider the setting where the observed networks have a shared expectation, but may differ in the noise structure on their edges. Our approach exploits the shared mean structure to denoise edge-level measurements of the observed networks and estimate the underlying population-level parameters. We also explore the extent to which edge-level errors influence estimation and downstream inference. We establish a finite-sample concentration inequality for the low-rank eigenvalue truncation of a random weighted adjacency matrix that may be of independent interest. The proposed approach is illustrated on synthetic networks and on data from an fMRI study of schizophrenia.