Linear regression and its inference on noisy network-linked data

TL;DR

This paper introduces a robust linear regression model for network-linked data that accounts for observational errors and network perturbations, providing reliable inference and demonstrating advantages over existing methods.

Contribution

It proposes a nonparametric network effects regression model that is robust to network observation errors and establishes an asymptotic inference framework under general error conditions.

Findings

The method is robust to network perturbations and observational errors.

Simulation results show improved accuracy and efficiency over existing methods.

Application to real data demonstrates practical effectiveness in social network analysis.

Abstract

Linear regression on network-linked observations has been an essential tool in modeling the relationship between response and covariates with additional network structures. Previous methods either lack inference tools or rely on restrictive assumptions on social effects and usually assume that networks are observed without errors. This paper proposes a regression model with nonparametric network effects. The model does not assume that the relational data or network structure is exactly observed and can be provably robust to network perturbations. Asymptotic inference framework is established under a general requirement of the network observational errors, and the robustness of this method is studied in the specific setting when the errors come from random network models. We discover a phase-transition phenomenon of the inference validity concerning the network density when no prior knowledge of the network model is available while also showing a significant improvement achieved by knowing the network model. Simulation studies are conducted to verify these theoretical results and demonstrate the advantage of the proposed method over existing work in terms of accuracy and computational efficiency under different data-generating models. The method is then applied to middle school students' network data to study the effectiveness of educational workshops in reducing school conflicts.