  We are concerned with modeling the strength of links in networks by taking
into account how often those links are used. Link usage is a strong indicator
of how closely two nodes are related, but existing network models in Bayesian
Statistics and Machine Learning are able to predict only wether a link exists
at all. As priors for latent attributes of network nodes we explore the Chinese
Restaurant Process (CRP) and a multivariate Gaussian with fixed dimensionality.
The model is applied to a social network dataset and a word coocurrence
dataset.
