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主讲人 |
Yingying Ma |
简介 |
<p>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 node 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 nodes. 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.</p> |
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主讲人简介 |
<p>Dr. Yingying Ma is an Associate Professor and Ph.D. advisor at the School of Economics and Management, Beihang University. Her research primarily focuses on spatio-temporal data modeling, social network analysis, and large-scale data sampling. She has received support from the National Natural Science Foundation of China (NSFC) through both General and Youth Programs. Dr. Ma has been recognized in Beihang University's Hundred Talents Program and the Young Top-Notch Talent Support Program. Her research has been published in leading journals, including the Journal of Econometrics (JOE) and the Journal of Business & Economic Statistics (JBES).</p> |
期数 |
高级计量经济学与统计学系列讲座2024年秋季学期第六讲（总178讲） |
