Estimation of subgraph density in noisy networks

TL;DR

This paper addresses the challenge of estimating subgraph densities in noisy networks, proposing method-of-moment estimators and a novel bootstrap approach for uncertainty quantification, especially when multiple network replicates are available.

Contribution

It introduces a new methodology for estimating subgraph densities with uncertainty quantification in noisy networks, including a bootstrap method for variance estimation.

Findings

Estimators are asymptotically normal as network size increases.

Confidence intervals can be constructed using the proposed bootstrap method.

Method performs well in gene coexpression network applications.

Abstract

While it is common practice in applied network analysis to report various standard network summary statistics, these numbers are rarely accompanied by uncertainty quantification. Yet any error inherent in the measurements underlying the construction of the network, or in the network construction procedure itself, necessarily must propagate to any summary statistics reported. Here we study the problem of estimating the density of an arbitrary subgraph, given a noisy version of some underlying network as data. Under a simple model of network error, we show that consistent estimation of such densities is impossible when the rates of error are unknown and only a single network is observed. Accordingly, we develop method-of-moment estimators of network subgraph densities and error rates for the case where a minimal number of network replicates are available. These estimators are shown to be asymptotically normal as the number of vertices increases to infinity. We also provide confidence intervals for quantifying the uncertainty in these estimates based on the asymptotic normality. To construct the confidence intervals, a new and non-standard bootstrap method is proposed to compute asymptotic variances, which is infeasible otherwise. We illustrate the proposed methods in the context of gene coexpression networks.