Recovering Network Structure from Aggregated Relational Data using Penalized Regression
Abstract
Social network data can be expensive to collect. Breza et al. (2017) propose aggregated relational data (ARD) as a lowcost 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 lowrank. As a consequence, network recovery from ARD is generally possible without parametric assumptions using a nuclearnorm penalized regression. We demonstrate how to implement this method and provide finitesample 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. Easytouse code in R and Python can be found at https://github.com/mpleung/ARD.
 Publication:

arXiv eprints
 Pub Date:
 January 2020
 DOI:
 10.48550/arXiv.2001.06052
 arXiv:
 arXiv:2001.06052
 Bibcode:
 2020arXiv200106052A
 Keywords:

 Economics  Econometrics;
 Economics  General Economics;
 Statistics  Applications