Recovering Network Structure from Aggregated Relational Data using Penalized Regression

Hossein Alidaee, Eric Auerbach and Michael Leung

Papers from arXiv.org

Abstract: Social network data can be expensive to collect. Breza et al. (2017) propose aggregated relational data (ARD) as a low-cost substitute that can be used to recover the structure of a latent social network when it is generated by a specific parametric random effects model. Our main observation is that many economic network formation models produce networks that are effectively low-rank. As a consequence, network recovery from ARD is generally possible without parametric assumptions using a nuclear-norm penalized regression. We demonstrate how to implement this method and provide finite-sample bounds on the mean squared error of the resulting estimator for the distribution of network links. Computation takes seconds for samples with hundreds of observations. Easy-to-use code in R and Python can be found at https://github.com/mpleung/ARD.

Date: 2020-01
New Economics Papers: this item is included in nep-ecm and nep-net
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