Supervised centrality via sparse network influence regression: an application to the 2021 Henan floods' social network

TL;DR

This paper introduces a supervised centrality measure tailored for social networks, specifically applied to Sina Weibo data from the 2021 Henan Floods, to identify influential users based on response variables.

Contribution

It develops a novel sparse network influence regression model with individual heterogeneity and a forward-addition algorithm for large networks, advancing influence analysis methods.

Findings

Successfully identified influential Sina Weibo users related to flood response

Demonstrated the method's effectiveness through simulation studies

Provided meaningful insights into social influence during emergencies

Abstract

The social characteristics of players in a social network are closely associated with their network positions and relational importance. Identifying those influential players in a network is of great importance as it helps to understand how ties are formed, how information is propagated, and, in turn, can guide the dissemination of new information. Motivated by a Sina Weibo social network analysis of the 2021 Henan Floods, where response variables for each Sina Weibo user are available, we propose a new notion of supervised centrality that emphasizes the task-specific nature of a player's centrality. To estimate the supervised centrality and identify important players, we develop a novel sparse network influence regression by introducing individual heterogeneity for each user. To overcome the computational difficulties in fitting the model for large social networks, we further develop a forward-addition algorithm and show that it can consistently identify a superset of the influential Sina Weibo users. We apply our method to analyze three responses in the Henan Floods data: the number of comments, reposts, and likes, and obtain meaningful results. A further simulation study corroborates the developed method.