Uncertainty quantification in noisy networks

In recent years there has been an explosion of network data from seemingly all corners of science – from computer science to engineering, from biology to physics, and from finance to sociology. We face analogues of many of the same fundamental types of problems encountered in a ‘Statistics 101’ course when analyzing network data. Despite roughly 20 years of research in the area, one of the fundamental capabilities that we still lack is quantifying uncertainty through propagation of network error. We conduct basic research laying statistical foundations for uncertainty quantification of this type, within a handful of key paradigms, focusing on problems ranging from epidemics to experiments on networks, when at least a few network replicates are available. Specifically, we study causal inference on noisy networks, and estimation of epidemic reproduction numbers in network-based and non-network-based settings. Ultimately, our work will bring critical insight into how ‘noise’ at the level of observed network connectivity impacts critical inferences and decisions derived from data in complex network systems.2022-04-07T00:00:00