Latent Space Models for Dynamic Networks with Weighted Edges
Methodology
2020-05-19 v1
Abstract
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.
Cite
@article{arxiv.2005.08261,
title = {Latent Space Models for Dynamic Networks with Weighted Edges},
author = {Daniel K. Sewell and Yuguo Chen},
journal= {arXiv preprint arXiv:2005.08261},
year = {2020}
}