A latent space mapping for link prediction

Network modeling can be approached using either discriminative or probabilistic
models. In the task of link prediction a probabilistic model will give a probability
for the existence of a link; while in some scenarios this may be beneficial, in others
a hard discriminative boundary needs to be set. Hence the use of a discriminative
classifier is preferable. In domains such as image analysis and speaker recognition,
probabilistic models have been used as a mechanism from which features
can be extracted. This paper examines using a probabilistic model built on the
entire graph to extract features to predict the existence of unknown links between
two nodes. It demonstrates how features extracted from the model as well as the
predicted probability of a link existing can aid the classification process.