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科学研究
RESEARCH
Network Hawkes Process Model with Latent Group Structure
时间  Datetime
2020-08-05 14:00 — 15:00
地点  Venue
Zoom APP(2)()
报告人  Speaker
朱雪宁
单位  Affiliation
复旦大学
邀请人  Host
王成
备注  remarks
会议号: 979 753 64509 会议密码: 751858
报告摘要  Abstract


Abstract: In this work, we study the event occurrences of user activities on online social network platforms. To characterize the social activity interactions among network users, we propose a network group Hawkes (NGH) process model. Particularly, the observed network structure information is employed to model the users’ dynamic posting behaviors. Furthermore, the users are clustered into latent groups according to their dynamic behavior patterns. To estimate the model, a constraint maximum likelihood approach is proposed. Theoretically, we establish the consistency and asymptotic normality of the estimators. In addition, we show that the group memberships can be identified consistently. To conduct estimation, a branching representation structure is firstly introduced, and a stochastic EM (StEM) algorithm is developed to tackle the computational problem. Lastly, we apply the proposed method to a social network data collected from Sina Weibo, and identify the influential network users as an interesting application.

 

 

个人介绍:朱雪宁,复旦大学大数据学院青年副研究员。2017年获得北京大学光华管理学院商务统计与经济计量系博士学位,之后在美国宾夕法尼亚州立大学从事博士后研究工作,并于2018年入职复旦大学大数据学院。入选2019年度上海市青年科技英才扬帆计划。主持国家自然科学基金青年基金一项,参与国家重大课题一项。主要研究领域为社交网络分析、高维数据建模等,研究成果发表于Journal of Econometrics, Annals of Statistics, Statistica Sinica、中国科学等国内外经济计量与统计学期刊。发表论文二十余篇,著有著作《R语言:从数据思维到数据实战》。