Segmenting Dynamic Network Data
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Networks and graphs arise naturally in many complex systems, often exhibiting dynamic behavior that can be modeled using dynamic networks. Two major research problems in dynamic networks are (1) community detection, which aims to find specific sub-structures within the networks, and (2) change point detection, which tries to find the time points at which sub-structures change. This paper proposes a new methodology to solve both problems simultaneously, using a model selection framework in which the Minimum Description Length Principle (MDL) is utilized as minimizing objective criterion. The derived detection algorithm is compatible with many existing methods, and is supported by empirical results and data analysis.
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