Pseudolikelihood EM for Within-network Relational Learning

Abstract

In this work, we study the problem of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">within-network</i> relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to predict the classes of unlabeled instances in the same graph. We categorize 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 our knowledge there has been no systematic comparison of models from all three categories. In this paper, we develop a novel pseudolikelihood EM method that facilitates more general <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collective</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning</i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collective</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inference</i> on partially labeled relational networks. We then compare this method to competing methods from the other categories on both synthetic and real-world data. We show that collective learning and inference with the pseudolikelihood EM approach achieves significantly higher accuracy than the other types of models when there are a moderate number of labeled examples in the data graph.