概率统计讨论班

报告题目：A Preferential Latent Space Model for Text Networks

报  告  人：蔡标 助理教授（香港城市大学）

时        间：2025年12月25日（星期四），上午11:00-12:00

地        点：海纳苑2幢206

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.

报告人简介：蔡标，香港城市大学决策分析及营运学系助理教授。他的主要兴趣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。