Supervised and unsupervised learning
methods have traditionally focused on data consisting of
<I>independent</I> instances of a single type.  However, many
real-world domains are best described by relational models in which
instances of multiple types are <I>related</I> to each other in
complex ways.  For example, in a scientific paper domain, papers are
related to each other via citation, and are also related to their
authors.  In this case, the label of one entity (e.g., the topic of
the paper) is often correlated with the labels of related entities.
We propose a general class of models for classification and clustering
in relational domains that capture probabilistic dependencies between
related instances.  We show how to learn such models efficiently
from data.  We present empirical results on two real world data sets.
Our experiments in a <I>transductive</I> classification setting
indicate that accuracy can be significantly improved by modeling
relational dependencies. Our algorithm automatically induces a very
natural behavior, where our knowledge about one instance helps us
classify related ones, which in turn help us classify others.  In an
unsupervised setting, our models produced coherent clusters with a
very natural interpretation, even for instance types that do not have
any attributes.
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