A Preferential Latent Space Model for Text Networks

主讲人：蔡标

蔡标，香港城市大学决策分析及营运学系助理教授。加入香港城市大学之前，他2021-2023在耶鲁大学生物统计系做博士后，2023-2024在美国辛辛那提大学担任数学系助理教授。2016年获中国科学技术大学统计学学士学位，2021年获美国迈阿密大学博士学位。他的主要研究方向是对于复杂结构数据的统计学习方法的提出和研究，包括点过程数据、张量数据、网络数据、基因组数据等。

1  报告信息

时间 

2026年5月27日(周三)

10:00

地点 

中国人民大学中关村校区明德主楼1016会议室

2  报告摘要

Network data enriched with textual information, referred to as text networks, arise in a wide range of applications, including email communications, scientific collaborations, and legal contracts. In such settings, both the structure of interactions (i.e., who connects with whom) and their content (i.e., what is communicated) are useful for understanding network relations. Traditional network analyses often focus only on the structure of the network and discard the rich textual information, resulting in an incomplete or inaccurate view of interactions. In this paper, we introduce a new modeling approach that incorporates texts into the analysis of networks using topic-aware text embedding, representing the text network as a generalized multi-layer network where each layer corresponds to a topic extracted from the data. We develop a new and flexible latent space network model that captures how node-topic preferences directly modulate edge formation, and establish identifiability conditions for the proposed model. We tackle model estimation with a projected gradient descent algorithm, and further discuss its theoretical properties. The efficacy of our proposed method is demonstrated through simulations and an analysis of an email network.

“统计大讲堂”由中国人民大学统计学院与应用统计科学研究中心联合主办，旨在搭建学界与业界的交流平台，促进统计理论与实践的深度融合，推动统计学科研创新，服务国家经济社会发展。作为统计学院人才培养体系的核心组成部分，“统计大讲堂”通过系列学术讲座、专题研讨等形式，为统计学子提供前沿知识传授与学术思维训练，营造开放包容的学术氛围，以培养具有国际视野、创新能力和实践精神的复合型统计人才。