Latent variable models for network data using fast inferential procedures. For more information please visit: < http://igollini.github.io/lvm4net/>.
lvm4net
provides a range of tools for latent variable models for network data. Most of the models are implemented using a fast variational inference approach. Latent space models for binary networks: the function lsm
implements the latent space model (LSM) introduced by Hoff et al. (2002) using a variational inference and squared Euclidian distance; the function lsjm
implements latent space joint model (LSJM) for multiplex networks introduced by Gollini and Murphy (2016). These models assume that each node of a network has a latent position in a latent space: the closer two nodes are in the latent space, the more likely they are connected.
Functions for binary bipartite networks will be added soon.
Gollini, I., and Murphy, T. B. (2016), "Joint Modelling of Multiple Network Views", Journal of Computational and Graphical Statistics, arXiv:1301.3759.
Hoff, P., Raftery, A., and Handcock, M. (2002), "Latent Space Approaches to Social Network Analysis", Journal of the American Statistical Association, 97, 1090--1098.