  Many systems of interacting elements can be conceptualized as networks, where
network nodes represent the elements and network ties represent interactions
between the elements. In systems where the underlying network evolves in time,
it is useful to determine the points in time where the network structure
changes significantly as these may correspond also to functional change points.
We propose a method for detecting these change points in correlation networks
that, unlike previous change point detection methods designed for time series
data, requires no distributional assumptions. We investigate the difficulty of
change point detection near the boundaries of data in correlation networks and
demonstrate the power of our method and a competing method through simulation.
We also show the generalizable nature of our method by applying it to stock
price data as well as fMRI data.
