  Inspired by empirical data on real world complex networks, the last few years
have seen an explosion in proposed generative models to understand and explain
observed properties of real world networks, including power law degree
distribution and "small world" distance scaling. In this context, a natural
question is the phenomenon of {\it change point}, understanding how abrupt
changes in parameters driving the network model change structural properties of
the network. We study this phenomenon in one popular class of dynamically
evolving networks: preferential attachment models. We derive asymptotic
properties of various functionals of the network including the degree
distribution as well as maximal degree asymptotics, in essence showing that the
change point does effect the degree distribution but does {\bf not} change the
degree exponent. This provides further evidence for long range dependence and
sensitive dependence of the evolution of the process on the initial evolution
of the process in such self-reinforced systems. We then propose an estimator
for the change point and prove consistency properties of this estimator. The
methodology developed highlights the effect of the non-ergodic nature of the
evolution of the network on classical change point estimators.
