Latent Space Models for Multidimensional Networks

Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) .

Latent space models for multivariate networks

Latent space models for binary multivariate networks (multiplex). The general model assumes that the nodes in the multiplex lie in a low-dimensional latent space. The probability of two nodes being connected is inversely related to their distance in this latent space: nodes close in the space are more likely to be linked, while nodes that are far apart are less likely to be connected. The model is defined in a hierarchical Bayesian framework and estimation is carried out via MCMC algorithm.

To install the development version from GitHub:
# install.packages("devtools")


D'Angelo, S., Murphy, T. B., Alfò, M. (2018).
Latent space modeling of multidimensional networks with application to the exchange of votes in Eurovision Song Contest.


Reference manual

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1.0.1 by Silvia D'Angelo, a year ago

Browse source code at

Authors: Silvia D'Angelo [aut, cre] , Michael Fop [aut] , Marco Alfò [ctb] , Thomas Brendan Murphy [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports MASS, permute, RcppTN, Rfast, sna, vegan

See at CRAN