Link-based Classification

Over the past few years, a number of approximate inference algorithms fornetworked 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. We also compare them across varying types of
correlations across links.