  In many complex systems, networks and graphs arise in a natural manner.
Often, time evolving behavior can be easily found and modeled using time-series
methodology. Amongst others, two common research problems in network analysis
are community detection and change-point detection. Community detection aims at
finding specific sub-structures within the networks, and change-point detection
tries to find the time points at which sub-structures change. We propose a
novel methodology to detect both community structures and change points
simultaneously based on a model selection framework in which the Minimum
Description Length Principle (MDL) is utilized as minimizing objective
criterion. The promising practical performance of the proposed method is
illustrated via a series of numerical experiments and real data analysis.
