  Changes in the structure of observed social and complex networks' structure
can indicate a significant underlying change in an organization, or reflect the
response of the network to an external event. Automatic detection of change
points in evolving networks is rudimentary to the research and the
understanding of the effect of such events on networks. Here we present an
easy-to-implement and fast framework for change point detection in temporal
evolving networks. Unlike previous approaches, our method is size agnostic, and
does not require either prior knowledge about the network's size and structure,
nor does it require obtaining historical information or nodal identities over
time. We use both synthetic data derived from dynamic models and two real
datasets: Enron email exchange and Ask-Ubuntu forum. Our framework succeeds
with both precision and recall and outperforms previous solutions
