@article {14429,
	title = {Empirical comparison of approximate inference algorithms for networked data},
	journal = {Open Problems in Statistical Relational Learning: Papers from the ICML Workshop. Pittsburgh, PA: www. cs. umd. edu/projects/srl2006},
	year = {2006},
	month = {2006///},
	abstract = {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.
},
	author = {Sen,P. and Getoor, Lise}
}
