Recovering shared structure from multiple networks with unknown edge distributions
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.
Cite
@article{arxiv.1906.07265,
title = {Recovering shared structure from multiple networks with unknown edge distributions},
author = {Keith Levin and Asad Lodhia and Elizaveta Levina},
journal= {arXiv preprint arXiv:1906.07265},
year = {2021}
}