We analyze the Relational Neighbor (RN) classifier as a simple
relational predictive model that predicts only based on class labels
of related neighbors, using no learning and no inherent attributes.
We show that it performs surprisingly well by comparing it to more
complex models such as Probabilistic Relational Models and Relational
Probability Trees on three data sets from published work. We argue
that a simple model such as this should be used as a baseline to
assess the performance of relational learners.