spaceNet: Latent Space Models for Multivariate Networks

In spaceNet: Latent Space Models for Multidimensional 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 [e-mail]

References

D'Angelo, S. and Murphy, T. B. and Alf<c3><b2>, 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 Alf<c3><b2>, M. and Murphy, T. B. (2018). Node-specific effects in latent space modelling of multidimensional networks. arXiv.

spaceNet documentation built on May 20, 2019, 1:03 a.m.