A two-way heterogeneity model for dynamic networks

Jiang, Binyan, Leng, Chenlei, Yan, Ting, Yao, Qiwei ORCID: 0000-0003-2065-8486 and Yu, Xinyang (2025) A two-way heterogeneity model for dynamic networks. Annals of Statistics. ISSN 0090-5364 (In Press)

Text (TWHM_revision) - Accepted Version Pending embargo until 1 January 2100. Available under License Creative Commons Attribution. Download (940kB)

Abstract

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 n observed networks each having p nodes, we develop a new asymptotic theory for the maximum likelihood estimation of 2p parameters when np → ∞. We overcome the global non-convexity 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.

Item Type:	Article
Divisions:	Statistics
Subjects:	Q Science > QA Mathematics
Date Deposited:	07 Jul 2025 07:03
Last Modified:	24 Jul 2025 08:57
URI:	http://eprints.lse.ac.uk/id/eprint/128638

Actions (login required)

View Item