Linear regression and its inference on noisy network-linked data

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.

References in corpus (17)

• Stochastic blockmodels and community structure in networks
• Homophily and Contagion Are Generically Confounded in Observational Social Network Studies
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Matrix estimation by Universal Singular Value Thresholding
• Modeling social networks from sampled data
• Fast community detection by SCORE
• Traceroute sampling makes random graphs appear to have power law degree distributions
• Accuracy and Scaling Phenomena in Internet Mapping
• Coauthorship and Citation Networks for Statisticians
• Estimating network structure from unreliable measurements
• Entrywise Eigenvector Analysis of Random Matrices with Low Expected Rank
• Estimating the number of communities in networks by spectral methods
• A Generic Sample Splitting Approach for Refined Community Recovery in Stochastic Block Models
• Unified ${\ell }_{2\to \infty }$ Eigenspace Perturbation Theory for Symmetric Random Matrices
• Consistency of Spectral Clustering on Hierarchical Stochastic Block Models
• Link prediction for egocentrically sampled networks
• Evaluating and Optimizing Network Sampling Designs: Decision Theory and Information Theory Perspectives

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