  We develop a Bayesian hierarchical model to identify communities in networks
for which we do not observe the edges directly, but instead observe a series of
interdependent signals for each of the nodes. Fitting the model provides an
end-to-end community detection algorithm that does not extract information as a
sequence of point estimates but propagates uncertainties from the raw data to
the community labels. Our approach naturally supports multiscale community
detection as well as the selection of an optimal scale using model comparison.
We study the properties of the algorithm using synthetic data and apply it to
daily returns of constituents of the S&P100 index as well as climate data from
US cities.
