2023/05/22 by Binyan Jiang, Jiang, Binyan, Chenlei Leng +7 · 3 citations
Biochemistry, Genetics and Molecular Biology · Economics, Econometrics and Finance · Physics and Astronomy · #62F12(Primary)62J99 #62R99(Secondary) #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Peroxisome Proliferator-Activated Receptors #Spatial and Panel Data Analysis #Statistics Theory (math.ST)
paper · doi:10.48550/arxiv.2305.12643
openalex publication_date 2023/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Dynamic network data analysis requires joint modelling individual snapshots and time dynamics. This paper proposes a new 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 of forming ties with other nodes and the other to differentiate the tendency of retaining existing ties over time. Though the negative log-likelihood function is non-convex, it is locally convex in a neighbourhood of the true value of the parameter vector. By using a novel method of moments estimator as the initial value, the consistent local maximum likelihood estimator (MLE) can be obtained by a gradient descent algorithm. To establish the upper bound for the estimation error of the MLE, we derive a new uniform deviation bound, which is of independent interest. The usefulness of the model and the associated theory are further supported by extensive simulation and the analysis of some real network data sets.