  Networks are representations of complex underlying social processes. However,
the same given network may be more suitable to model one behavior of
individuals than another. In many cases, aggregate population models may be
more effective than modeling on the network. We present a general framework for
evaluating the suitability of given networks for a set of predictive tasks of
interest, compared against alternative, networks inferred from data. We present
several interpretable network models and measures for our comparison. We apply
this general framework to the case study on collective classification of music
preferences in a newly available dataset of the Last.fm social network.
