Empirical comparison of approximate inference algorithms for networked data

Over the past few years, a number of approx-imate inference algorithms for networked data
have been put forth. We empirically compare the
performance of three of the popular algorithms:
loopy belief propagation, mean field relaxation
labeling and iterative classification. We rate each
algorithm in terms of its robustness to noise, both
in attribute values and correlations across links.
A novel observation from our experiments is that
loopy belief propagation faces difficulty when in-
ferring over data with homophily, a common type
of link correlation observed in relational data.