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Estimation of subgraph densities in noisy networks

Chang, Jinyuan, Kolaczyk, Eric D. and Yao, Qiwei (2020) Estimation of subgraph densities in noisy networks. Journal of the American Statistical Association. ISSN 0162-1459

[img] Text (Estimation of subgraph densities in noisy networks) - Accepted Version
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Identification Number: 10.1080/01621459.2020.1778482

Abstract

While it is common practice in applied network analysis to report various stan- dard network summary statistics, these numbers are rarely accompanied by un- certainty 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 esti- mates based on the asymptotic normality. To construct the confidence intervals, a new and non-standard bootstrap method is proposed in order to compute asymp- totic variances, which is infeasible otherwise. We illustrate the proposed methods in the context of gene coexpression networks.

Item Type: Article
Official URL: https://www.tandfonline.com/toc/uasa20/current
Additional Information: © 2020 American Statistical Association
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 02 Jun 2020 14:39
Last Modified: 07 Jul 2020 10:09
URI: http://eprints.lse.ac.uk/id/eprint/104684

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