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oai:figshare.com:article/34047159 2026-10-01T16:36:32Z portal_1197 item_type_6 month_year_10_2026

Supervised centrality via sparse network influence regression : an application to the 2021 Henan floods' social network Yingying Ma (3617810) Wei Lan (1390307) Chenlei Leng (805971) Hansheng Wang (1390308) Migrated from ePrints 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. 2025-06-01T00:00:00Z Text Journal contribution 10.1214/24-AOAS2008 https://figshare.com/articles/journal_contribution/Supervised_centrality_via_sparse_network_influence_regression_an_application_to_the_2021_Henan_floods_social_network/34047159 CC BY 4.0