  Learning the right graph representation from noisy, multisource data has
garnered significant interest in recent years. A central tenet of this problem
is relational learning. Here the objective is to incorporate the partial
information each data source gives us in a way that captures the true
underlying relationships. To address this challenge, we present a general,
boosting-inspired framework for combining weak evidence of entity associations
into a robust similarity metric. We explore the extent to which different
quality measurements yield graph representations that are suitable for
community detection. We then present empirical results on both synthetic and
real datasets demonstrating the utility of this framework. Our framework leads
to suitable global graph representations from quality measurements local to
each edge. Finally, we discuss future extensions and theoretical considerations
of learning useful graph representations from weak feedback in general
application settings.
