README.md

In michaelfop/spaceNet: Latent Space Models for Multidimensional Networks

spaceNet

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.

The package is available on CRAN, to install it:

install.packages("spaceNet")

To install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("michaelfop/spaceNet")

References

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. arXiv:1803.07166.

michaelfop/spaceNet documentation built on May 6, 2019, 8:58 p.m.