主 题：Modeling networks with textual edges带有文本边的网络建模

主讲人：埃默里大学张菁菲教授

主持人：统计学院林华珍教授

时间：2024年6月6日（周四）下午4：30-5：30

举办地点：柳林校区弘远楼408会议室

主办单位：统计研究中心和统计学院 科研处

主讲人简介：

Dr. Emma Jingfei Zhang is an Associate Professor of Information Systems & Operations Research at the Goizueta Business School of Emory University. She also holds a secondary appointment in the Department of Biostatistics & Bioinformatics at the Rollins School of Public Health of Emory University. Her research focuses on the developments of statistical methods and theory for networks, graphs, tensors, and point processes, with applications in biology, medicine, and business. She serves as an associate editor of the Annals of Applied Statistics, Computational Statistics & Data Analysis, Statistica Sinica and the Journal of American Statistical Association.

Emma Jingfei Zhang博士是埃默里大学Goizueta商学院信息系统与运筹学的副教授，同时在埃默里大学Rollins公共卫生学院的生物统计与生物信息学系担任副职。她的研究集中在网络、图、张量和点过程的统计方法和理论的发展，应用领域包括生物学、医学和商业。她担任《Annals of Applied Statistics》、《Computational Statistics & Data Analysis》、《Statistica Sinica》和《Journal of American Statistical Association》的副主编。

内容简介：

Edges in many real-world networks are associated with rich text information, such as email communications between accounts and interactions between social media users. To better account for the rich text information, we propose a new latent space network model that treats texts as embedded vectors. We establish a set of identifiability conditions for the proposed model and formulate a projected gradient descent algorithm for model estimation. We further investigate theoretical properties of the iterates from the proposed algorithm. The efficacy of our method is demonstrated through simulations and an analysis of the Enron email dataset.

许多现实世界的网络中的边都与丰富的文本信息相关联，例如账户间的电子邮件通信和社交媒体用户之间的互动。为了更好地利用这些丰富的文本信息，主讲人提出了一种新的潜在空间网络模型，将文本视为嵌入向量。我们为该模型建立了一组可识别性条件，并提出了一种投影梯度下降算法用于模型估计。主讲人进一步研究了该算法迭代过程的理论性质。通过模拟和对安然电子邮件数据集的分析，证明了主讲人方法的有效性。