  Learning the right graph representation from noisy, multi-source 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. Building on previous work, we explore the
extent to which different local quality measurements yield graph
representations that are suitable for community detection. We present empirical
results on a variety of datasets demonstrating the utility of this framework,
especially with respect to real datasets where noise and scale present serious
challenges. Finally, we prove a convergence theorem in an ideal setting and
outline future research into other application domains.
