Pseudo-Likelihood Ratio Screening based on Network Data with Applications

TL;DR

This paper proposes a pseudo-likelihood ratio screening method for high-dimensional categorical features in network data, effectively incorporating network structure to improve preference analysis accuracy.

Contribution

It introduces a novel screening procedure that accounts for network structure and categorical features, with theoretical validation and empirical testing.

Findings

The method accurately identifies relevant features in simulated data.

Application to Sina Weibo data demonstrates improved prediction performance.

Theoretical analysis confirms the procedure's consistency and robustness.

Abstract

Social network platforms today generate vast amounts of data, including network structures and a large number of user-defined tags, which reflect users' interests. The dimensionality of these personalized tags can be ultra-high, posing challenges for model analysis in targeted preference analysis. Traditional categorical feature screening methods overlook the network structure, which can lead to incorrect feature set and suboptimal prediction accuracy. This study focuses on feature screening for network-involved preference analysis based on ultra-high-dimensional categorical tags. We introduce the concepts of self-related features and network-related features, defined as those directly related to the response and those related to the network structure, respectively. We then propose a pseudo-likelihood ratio feature screening procedure that identifies both types of features. Theoretical properties of this procedure under different scenarios are thoroughly investigated. Extensive simulations and real data analysis on Sina Weibo validate our findings.