Many real-world domains are relational in nature, consisting of a set
of objects related to each other in complex ways. This paper focuses on
predicting the existence and the type of links between entities in such
domains. We apply the relational
Markov network framework of Taskar et al. to define a joint
probabilistic model over the entire link graph entity attributes and
links. The application of the RMN algorithm to this task requires the
definition of probabilistic patterns over subgraph structures. We apply this
method to two new relational datasets, one involving university webpages,
and the other a social network. We show that the collective classification
approach of RMNs, and the introduction of subgraph patterns over link
labels, provide significant improvements in accuracy over flat
classification, which attempts to predict each link in isolation.