A0438
Title: Supervised centrality via sparse network influence regression with an application to 2021 Henan floods social network
Authors: Yingying Ma - Beihang University (China) [presenting]
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 and how information is propagated, and in turn, can guide the dissemination of new information. Motivated by a Sina Weibo social network on 2021 Henan Floods where response variables on each node are available, a new notion of supervised centrality is proposed, emphasizing the fact that the centrality of a player is task-specific. To estimate the supervised centrality and identify important players, a novel sparse spatial autoregression is developed by introducing individual heterogeneity to each user. To overcome the computational difficulties in fitting the model for large social networks, a forward-addition algorithm is further developed and shown to consistently identify a superset of the influential nodes. The method is applied to analyze three responses in Henan Floods data: the number of comments, reposts and likes, and obtain meaningful results. The simulation study further corroborates the developed theory.
