  Networks are complex models for underlying data in many application domains.
In most instances, raw data is not natively in the form of a network, but
derived from sensors, logs, images, or other data. Yet, the impact of the
various choices in translating this data to a network have been largely
unexamined. In this work, we propose a network model selection methodology that
focuses on evaluating a network's utility for varying tasks, together with an
efficiency measure which selects the most parsimonious model. We demonstrate
that this network definition matters in several ways for modeling the behavior
of the underlying system.
