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.

中文翻译：

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网络时间序列中多个变更点和社区结构的同时检测

在许多复杂的系统中，网络和图形以自然的方式出现。通常，可以使用时间序列方法轻松找到时间演变行为并对其建模。其中，网络分析中的两个常见研究问题是社区检测和更改点检测。社区检测旨在发现网络中特定的子结构，而变更点检测则试图找到子结构发生变化的时间点。我们提出了一种新的方法，该方法基于模型选择框架同时检测社区结构和变更点，在模型选择框架中，最小描述长度原则（MDL）被用作最小化客观标准。通过一系列数值实验和实际数据分析说明了该方法的有希望的实用性能。