Sequential estimation of temporally evolving latent space network models

Kathryn Turnbull, Christopher Nemeth, Matthew Nunes, Tyler Mccormick

Research output: Contribution to journalArticlepeer-review

Abstract

Dynamic network data describe interactions among a fixed population through time. This data type can be modelled using the latent space framework, where the probability of a connection forming is expressed as a function of low-dimensional latent coordinates associated with the nodes, and sequential estimation of model parameters can be achieved 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. A novel approach to sequentially infer parameters of dynamic latent space network models is proposed by building on techniques from the high-dimensional SMC literature. The scalability and performance of the proposed approach is explored via simulation, and the flexibility under model variants is demonstrated. Finally, a real-world dataset describing classroom contacts is analysed using the proposed methodology.
Original languageEnglish
Article number107627
JournalComputational Statistics & Data Analysis
Volume179
Early online date12 Oct 2022
DOIs
Publication statusE-pub ahead of print - 12 Oct 2022

Keywords

  • Dynamic networks
  • Latent space
  • Sequential Monte Carlo
  • Statistical network analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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