2021/12/19 by Kathryn Turnbull, Christopher Nemeth, Turnbull, Kathryn +5
Mathematics · Neuroscience · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Methodology (stat.ME) #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2112.10220
openalex publication_date 2021/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this article we focus on dynamic network data which describe interactions among a fixed population through time. We model this data using the latent space framework, in which the probability of a connection forming is expressed as a function of low-dimensional latent coordinates associated with the nodes, and consider sequential estimation of model parameters via Sequential Monte Carlo (SMC) methods. In this setting, SMC is a natural candidate for estimation which offers greater scalability than existing approaches commonly considered in the literature, allows for estimates to be conveniently updated given additional observations and facilitates both online and offline inference. We present a novel approach to sequentially infer parameters of dynamic latent space network models by building on techniques from the high-dimensional SMC literature. Furthermore, we examine the scalability and performance of our approach via simulation, demonstrate the flexibility of our approach to model variants and analyse a real-world dataset describing classroom contacts.