报告题目：Temporal network analysis via a degree-corrected Cox model

报 告 人：曲连强 副教授 华中师范大学

报告时间：2026年1月16日 10：00-11：00

报告地点：腾讯会议540549350

校内联系人：王培洁 [email protected]

报告摘要：Temporal dynamics, characterised by time-varying degree heterogeneity and homophily effects, are often exhibited in many real-world networks. As observed in an MIT Social Evolution study, the in-degree and out-degree of the nodes show considerable heterogeneity that varies with time. Concurrently, homophily effects, which explain why nodes with similar characteristics are more likely to connect with each other, are also time-dependent. To facilitate the exploration and understanding of these dynamics, we propose a novel degree-corrected Cox network model for directed networks, where the way for degree-heterogeneity or homophily effects to change with time is left completely unspecified. Because each node has individual-specific in- and out-degree parameters that vary over time,the number of unknown parameters grows with the number of nodes, leading to a high-dimensional estimation problem. Therefore, making statistical inference is highly nontrivial.We develop a local estimating equations approach to estimate the unknown parameters and establish the consistency and asymptotic normality of the proposed estimators in the high-dimensional regime. We further propose test statistics to check whether temporal variation or degree heterogeneity is present in the network and develop a graphical diagnostic method to evaluate goodness-of-fit for dynamic network models. Simulation studies and two real data analyses are provided to assess the finite sample performance of the proposed method and illustrate its practical utility.

报告人简介：曲连强现为华中师范大学副教授。主要研究方向为生存分析和大规模复杂数据统计推断。现已发表学术论文18篇，包括JASA、Biometrika，JMLR 以及JBES等。