  Relational learning in networked data has been shown to be effective in a
number of studies. Relational learners, composed of relational classifiers and
collective inference methods, enable the inference of nodes in a network given
the existence and strength of links to other nodes. These methods have been
adapted to predict customer churn in telecommunication companies showing that
incorporating them may give more accurate predictions. In this research, the
performance of a variety of relational learners is compared by applying them to
a number of CDR datasets originating from the telecommunication industry, with
the goal to rank them as a whole and investigate the effects of relational
classifiers and collective inference methods separately. Our results show that
collective inference methods do not improve the performance of relational
classifiers and the best performing relational classifier is the network-only
link-based classifier, which builds a logistic model using link-based measures
for the nodes in the network.
