Relational Classifiers in a Non-relational World: Using Homophily to Create Relations

Abstract

Research in the past decade on statistical relational learning (SRL) has shown the power of the underlying network of relations in relational data. Even models built using only relations often perform comparably to models built using sophisticated relational learning methods. However, many data sets -- such as those in the UCI machine learning repository -- contain no relations. In fact, many data sets either do not contain relations or have relations which are not helpful to a specific classification task. The question we investigate in this paper is whether it is possible to construct relations such that relational inference results in better classification performance than non-relational inference. Using simple similarity-based rules to create relations and weighting the strength of these relations using homophily on instance labels, we test whether relational inference techniques are applicable -- in other words, do they perform comparably to standard machine learning algorithms. We show, in an experimental study on 31 UCI benchmark data sets, that relational inference wins more than any of the 6 classifiers we compare against, including a transductive SVM, and that it wins the majority of the time when compared against any one of them.