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• W2950796323 abstract "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." @default.
• W2950796323 created "2019-06-27" @default.
• W2950796323 creator A5022402974 @default.
• W2950796323 creator A5026493959 @default.
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• W2950796323 date "2019-06-13" @default.
• W2950796323 modified "2023-09-27" @default.
• W2950796323 title "Recovering low-rank structure from multiple networks with unknown edge distributions." @default.
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• W2950796323 hasPublicationYear "2019" @default.
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