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Abstract:
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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. In network analysis, research problems can be largely divided into two categories: 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 analyses.
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