  A neighborhood graph, which represents the instances as vertices and their
relations as weighted edges, is the basis of many semi-supervised and
relational models for node labeling and link prediction. Most methods employ a
sequential process to construct the neighborhood graph. This process often
consists of generating a candidate graph, pruning the candidate graph to make a
neighborhood graph, and then performing inference on the variables (i.e.,
nodes) in the neighborhood graph. In this paper, we propose a framework that
can dynamically adapt the neighborhood graph based on the states of variables
from intermediate inference results, as well as structural properties of the
relations connecting them. A key strength of our framework is its ability to
handle multi-relational data and employ varying amounts of relations for each
instance based on the intermediate inference results. We formulate the link
prediction task as inference on neighborhood graphs, and include preliminary
results illustrating the effects of different strategies in our proposed
framework.
