  We investigate the community detection problem on graphs in the existence of
multiple edge types. Our main motivation is that similarity between objects can
be defined by many different metrics and aggregation of these metrics into a
single one poses several important challenges, such as recovering this
aggregation function from ground-truth, investigating the space of different
clusterings, etc. In this paper, we address how to find an aggregation function
to generate a composite metric that best resonates with the ground-truth. We
describe two approaches: solving an inverse problem where we try to find
parameters that generate a graph whose clustering gives the ground-truth
clustering, and choosing parameters to maximize the quality of the ground-truth
clustering. We present experimental results on real and synthetic benchmarks.
