报告人：于鑫洋（香港理工大学）
报告时间：2026年10月16日（周五）上午9：00 - 11：00
报告地点：6号楼C405
报告摘要：Analysis of networks that evolve dynamically requires the joint modelling of individual snapshots and time dynamics. This paper proposes a new flexible two-way heterogeneity model towards this goal. The new model equips each node of the network with two heterogeneity parameters, one to characterize the propensity to form ties with other nodes statically and the other to differentiate the tendency to retain existing ties over time. With observed networks each having p nodes, we develop a new asymptotic the ory for the maximum likelihood estimation of 2p parameters when np → ∞. We overcome the global nonconvexity of the negative log-likelihood function by the virtue of its local convexity, and propose a novel method of moment estimator as the initial value for a simple algorithm that leads to the consistent local maximum likelihood estimator (MLE). To establish the upper bounds for the estimation error of the MLE, we derive a new uniform deviation bound, which is of independent interest. The theory of the model and its usefulness are further supported by extensive simulation and the analysis of some real network data sets.
