时 间：2026年4月21日（周二） 15：00 – 16：30
地 点：普陀校区理科大楼A1514室
报告人：冷琛雷 香港理工大学教授
邀请人：谌自奇 华东师范大学研究员
摘 要：
Reciprocity--the tendency of individuals to form mutual ties--is a fundamental structural feature of many directed networks. Despite its ubiquity, reciprocity remains insufficiently integrated into statistical network models, particularly in relation to covariate information. In this paper, we introduce the R2-Model, a novel and flexible framework that explicitly models reciprocity while incorporating covariate effects. Built upon a generalized p1 model, our framework accommodates both network sparsity and node heterogeneity, offering the most comprehensive parametrization of reciprocity to date--capturing not only its baseline level but also how it systematically varies with observed covariates. To address the challenges posed by high dimensionality and nuisance parameters, we develop a conditional likelihood estimator that isolates and consistently estimates the reciprocity effects. We establish its theoretical guarantees, including consistency, asymptotic normality, and minimax optimality under broad sparsity regimes. Extensive simulations and real-world applications demonstrate the R2-Model's flexibility, interpretability, and strong finite-sample performance, highlighting its practical utility for uncovering covariate-driven patterns of reciprocity in directed networks.
报告人简介：
Chenlei Leng is Chair Professor of Statistics and Machine Learning in the Department of Applied Mathematics at the Hong Kong Polytechnic University. His research focuses on developing novel statistical and machine learning methodologies for large and complex data, with an emphasis on high-dimensional, correlated, and network-structured data. A Fellow of the Institute of Mathematical Statistics and an Elected Member of the International Statistical Institute, Professor Leng has held several notable leadership roles, including Chair of the Research Section of the Royal Statistical Society, Co-Director of the Oxford–Warwick Centre for Doctoral Training, and as an inaugural Turing Fellow at the Alan Turing Institute.