  We introduce a community detection method that finds clusters in network
time-series by introducing an algorithm that finds significantly interconnected
nodes across time. These connections are either increasing, decreasing, or
constant over time. Significance of nodal connectivity within a set is judged
using the Weighted Configuration Null Model at each time-point, then a novel
significance-testing scheme is used to assess connectivity at all time points
and the direction of its time-trend. We apply this method to bikeshare networks
in New York City and Chicago and taxicab pickups and dropoffs in New York to
find and illustrate patterns in human mobility in urban zones. Results show
stark geographical patterns in clusters that are growing and declining in
relative usage across time and potentially elucidate latent economic or
demographic trends.
