Abstract
Recent work on graphical models for relational data has demonstrated significant
improvements in classification and inference when models represent the dependen-
cies among instances. Despite its use in conventional statistical models, the as-
sumption of instance independence is contradicted by most relational datasets. For
example, in citation data there are dependencies among the topics of a paper’s
references, and in genomic data there are dependencies among the functions of
interacting proteins. In this chapter we present relational dependency networks
(RDNs), a graphical model that is capable of expressing and reasoning with such
dependencies in a relational setting. We discuss RDNs in the context of relational
Bayes networks and relational Markov networks and outline the relative strengths
of RDNs—namely, the ability to represent cyclic dependencies, simple methods for
parameter estimation, and efficient structure learning techniques. The strengths of
RDNs are due to the use of pseudolikelihood learning techniques, which estimate an
efficient approximation of the full joint distribution. We present learned RDNs for
a number of real-world datasets and evaluate the models in a prediction context,
showing that RDNs identify and exploit cyclic relational dependencies to achieve
significant performance gains over conventional conditional models.
