| spaceNet {spaceNet} | R Documentation |
Latent Space Models for Multivariate Networks
Description
A package for latent space models for binary multivariate networks (multiplex). The 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 allows the inclusion of node-specific sender and receiver effects and edge-specific covariates. Inference is carried out via a MCMC algorithm.
Details
The main function is multiNet, which estimates the latent space model via MCMC algorithm. Data can be inputed either as a list or an array. Also, edge-specific covariates in the form of a list or an array can be included in the model.
Author(s)
Silvia D'Angelo and Michael Fop.
Mantainer: Silvia D'Angelo silvia.dangelo@ucd.ie
References
D'Angelo, S. and Murphy, T. B. and Alfò, M. (2018). Latent space modeling of multidimensional networks with application to the exchange of votes in the Eurovision Song Contest. arXiv.
D'Angelo, S. and Alfò, M. and Murphy, T. B. (2018). Node-specific effects in latent space modelling of multidimensional networks. arXiv.