Relational Similarity Machines

Abstract

In this work, we propose Relational Similarity Machines (RSM) a fast, accurate, and flexible relational learning framework for supervised and semi-supervised classification problems. Despite the importance and wide-scale applicability of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalability, accuracy, and flexibility for handling a wide variety of classification problems, data, constraints, and tasks. For instance, many existing methods perform poorly for multi-class classification problems, graphs that are sparsely labeled or exhibit low relational autocorrelation, among many others. In contrast, our proposed framework RSM is designed to be (i) fast for learning and inference at real-time interactive rates, and (ii) flexible for a variety of learning settings (multi-class problems), constraints (few labeled instances), and application domains. The experiments demonstrate the effectiveness of RSM for a variety of tasks and data.