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In this paper, the authors study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to predict the classes of unlabeled instance in the same graph. They categorized recent work in statistical relational learning into three alternative approaches for this setting: disjoint learning with disjoint inference, disjoint learning with collective inference, and collective learning with collective inference. Models from each of these categories has been employed previously in different settings, but to the knowledge there has been no systematic comparison of models from all three categories.
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