VARIATIONAL INFERENCE FOR LATENT SPACE MODELS FOR DYNAMIC NETWORKS

Yan Liu, Yuguo Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Latent space models are popular for analyzing dynamic network data. We propose a variational approach to estimate the model parameters and the latent positions of the nodes in the network. The proposed approach is much faster than Markov chain Monte Carlo algorithms, and is able to handle large networks. Theoretical properties of the variational Bayes risk of the proposed procedure are provided. We apply the variational method with the latent space model to simulated and real data to demonstrate its performance.

Original languageEnglish (US)
Pages (from-to)2147-2170
Number of pages24
JournalStatistica Sinica
Volume32
Issue number4
DOIs
StatePublished - Oct 2022

Keywords

  • Bayes risk
  • dynamic network
  • latent space model
  • variational inference

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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