  A number of real world problems in many domains (e.g. sociology, biology,
political science and communication networks) can be modeled as dynamic
networks with nodes representing entities of interest and edges representing
interactions among the entities at different points in time. A common
representation for such models is the snapshot model - where a network is
defined at logical time-stamps. An important problem under this model is change
point detection. In this work we devise an effective and efficient
three-step-approach for detecting change points in dynamic networks under the
snapshot model. Our algorithm achieves up to 9X speedup over the
state-of-the-art while improving quality on both synthetic and real world
networks.
