  This paper proposes Relational Similarity Machines (RSM): a fast, accurate,
and flexible relational learning framework for supervised and semi-supervised
learning tasks. Despite the importance 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 network data with low relational autocorrelation.
In contrast, the proposed relational learning framework 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.
