  Recent years have witnessed a rise in real-world data captured with rich
structural information that can be conveniently depicted by multi-relational
graphs. While inference of continuous node features across a simple graph is
rather under-studied by the current relational learning research, we go one
step further and focus on node regression problem on multi-relational graphs.
We take inspiration from the well-known label propagation algorithm aiming at
completing categorical features across a simple graph and propose a novel
propagation framework for completing missing continuous features at the nodes
of a multi-relational and directed graph. Our multi-relational propagation
algorithm is composed of iterative neighborhood aggregations which originate
from a relational local generative model. Our findings show the benefit of
exploiting the multi-relational structure of the data in several node
regression scenarios in different settings.
