Latent space models for dynamic networks with weighted edges

Daniel K. Sewell, Yuguo Chen

Research output: Contribution to journalArticlepeer-review


Longitudinal binary relational data can be better understood by implementing a latent space model for dynamic networks. This approach can be broadly extended to many types of weighted edges by using a link function to model the mean of the dyads, or by employing a similar strategy via data augmentation. To demonstrate this, we propose models for count dyads and for non-negative real dyads, analyzing simulated data and also both mobile phone data and world export/import data. The model parameters and latent actors' trajectories, estimated by Markov chain Monte Carlo algorithms, provide insight into the network dynamics.

Original languageEnglish (US)
Pages (from-to)105-116
Number of pages12
JournalSocial Networks
StatePublished - Jan 1 2016


  • Embedding
  • Markov chain Monte Carlo
  • Network dynamics
  • Valued dyad
  • Visualization
  • Weighted network

ASJC Scopus subject areas

  • Anthropology
  • Sociology and Political Science
  • General Social Sciences
  • General Psychology


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