  The topology of social networks can be understood as being inherently
dynamic, with edges having a distinct position in time. Most characterizations
of dynamic networks discretize time by converting temporal information into a
sequence of network "snapshots" for further analysis. Here we study a highly
resolved data set of a dynamic proximity network of 66 individuals. We show
that the topology of this network evolves over a very broad distribution of
time scales, that its behavior is characterized by strong periodicities driven
by external calendar cycles, and that the conversion of inherently
continuous-time data into a sequence of snapshots can produce highly biased
estimates of network structure. We suggest that dynamic social networks exhibit
a natural time scale \Delta_{nat}, and that the best conversion of such dynamic
data to a discrete sequence of networks is done at this natural rate.
