  For any stream of time-stamped edges that form a dynamic network, an
important choice is the aggregation granularity that an analyst uses to bin the
data. Picking such a windowing of the data is often done by hand, or left up to
the technology that is collecting the data. However, the choice can make a big
difference in the properties of the dynamic network. This is the time scale
detection problem. In previous work, this problem is often solved with a
heuristic as an unsupervised task. As an unsupervised problem, it is difficult
to measure how well a given algorithm performs. In addition, we show that the
quality of the windowing is dependent on which task an analyst wants to perform
on the network after windowing. Therefore the time scale detection problem
should not be handled independently from the rest of the analysis of the
network.
  We introduce a framework that tackles both of these issues: By measuring the
performance of the time scale detection algorithm based on how well a given
task is accomplished on the resulting network, we are for the first time able
to directly compare different time scale detection algorithms to each other.
Using this framework, we introduce time scale detection algorithms that take a
supervised approach: they leverage ground truth on training data to find a good
windowing of the test data. We compare the supervised approach to previous
approaches and several baselines on real data.
