  We address the problem of defining a network graph on a large collection of
classes. Each class is comprised of a collection of data points, sampled in a
non i.i.d. way, from some unknown underlying distribution. The application we
consider in this paper is a large scale high dimensional survey of people
living in the US, and the question of how similar or different are the various
counties in which these people live. We use a co-clustering diffusion metric to
learn the underlying distribution of people, and build an approximate earth
mover's distance algorithm using this data adaptive transportation cost.
