Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7538531 | Social Networks | 2016 | 12 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Daniel K. Sewell, Yuguo Chen,