  The probabilistic graphs framework models the uncertainty inherent in
real-world domains by means of probabilistic edges whose value quantifies the
likelihood of the edge existence or the strength of the link it represents. The
goal of this paper is to provide a learning method to compute the most likely
relationship between two nodes in a framework based on probabilistic graphs. In
particular, given a probabilistic graph we adopted the language-constraint
reachability method to compute the probability of possible interconnections
that may exists between two nodes. Each of these connections may be viewed as
feature, or a factor, between the two nodes and the corresponding probability
as its weight. Each observed link is considered as a positive instance for its
corresponding link label. Given the training set of observed links a
L2-regularized Logistic Regression has been adopted to learn a model able to
predict unobserved link labels. The experiments on a real world collaborative
filtering problem proved that the proposed approach achieves better results
than that obtained adopting classical methods.
