SUPERVISED CENTRALITY VIA SPARSE NETWORK INFLUENCE REGRESSION: AN APPLICATION TO THE 2021 HENAN FLOODS' SOCIAL NETWORK

• 成果类型: Article
• 署名作者: Ma, Yingying; Lan, Wei; Leng, Chenlei; Li, Ting; Wang, Hansheng
• 署名单位: Beihang University; Southwestern University of Finance & Economics - China; University of Warwick; Hong Kong Polytechnic University; Peking University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS2008
• 发表日期: 2025
• 页码: 1734-1752
• 关键词: local-aggregate; MODEL; autocorrelation; prediction

摘要

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.