Regression of binary network data with exchangeable latent errors

TL;DR

This paper introduces the PX model for binary network data, providing a computationally efficient way to estimate regression effects while accounting for dependencies, demonstrated through simulations and real data analysis.

Contribution

The paper proposes the Probit Exchangeable (PX) model, a new exchangeability-based approach that simplifies estimation of binary network data with improved efficiency.

Findings

PX model accurately estimates regression coefficients.

PX model reduces computational runtime significantly.

PX model maintains predictive performance in real data.

Abstract

Undirected, binary network data consist of indicators of symmetric relations between pairs of actors. Regression models of such data allow for the estimation of effects of exogenous covariates on the network and for prediction of unobserved data. Ideally, estimators of the regression parameters should account for the inherent dependencies among relations in the network that involve the same actor. To account for such dependencies, researchers have developed a host of latent variable network models, however, estimation of many latent variable network models is computationally onerous and which model is best to base inference upon may not be clear. We propose the Probit Exchangeable (PX) model for undirected binary network data that is based on an assumption of exchangeability, which is common to many of the latent variable network models in the literature. The PX model can represent the first two moments of any exchangeable network model. We leverage the EM algorithm to obtain an approximate maximum likelihood estimator of the PX model that is extremely computationally efficient. Using simulation studies, we demonstrate the improvement in estimation of regression coefficients of the proposed model over existing latent variable network models. In an analysis of purchases of politically-aligned books, we demonstrate political polarization in purchase behavior and show that the proposed estimator significantly reduces runtime relative to estimators of latent variable network models, while maintaining predictive performance.