Abstract
When entities are linked by explicit relations, classification methods
that take advantage of the network can perform substantially better
than methods that ignore the network. This paper argues that studies
of relational classification in networked data should include simple
network-only methods as baselines for comparison, in addition to the
non-relational baselines that generally are used. In particular,
comparing more complex algorithms with algorithms that only consider
the network (and not the features of the entities) allows one to
factor out the contribution of the network structure itself to the
predictive power of the model. We examine several simple methods for
network-only classification on previously used relational data sets,
and show that they can perform remarkably well. The results
demonstrate that the inclusion of network-only classifiers can shed
new light on studies of relational learners.