Sequential Estimation of Temporally Evolving Latent Space Network Models

TL;DR

This paper introduces a scalable sequential Monte Carlo method for estimating parameters in dynamic latent space network models, enabling efficient online inference for evolving network data.

Contribution

It presents a novel SMC-based approach for sequentially estimating parameters in dynamic latent space models, improving scalability and flexibility over existing methods.

Findings

Method performs well in simulations

Approach adapts to different model variants

Successfully applied to real-world classroom contact data

Abstract

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.