演化网络中与规模无关的变点检测框架
Size Agnostic Change Point Detection Framework for Evolving Networks
摘要：
观察到的社会和复杂网络结构的变化可以表明组织中的重大潜在变化，或者反映网络对外部事件的反应。自动检测演化网络中的变点是研究和理解此类事件对网络影响的基础。在这里，我们提出了一个易于实现和快速的框架，在时间演化网络的变化点检测。与以前的方法不同，我们的方法是大小不可知的，既不需要关于网络的大小和结构的先验知识，也不需要随着时间的推移获得历史信息或节点身份。我们使用的合成数据来自动态模型和两个真实的数据集：安然电子邮件交换和Ask-Ubuntu论坛。我们的框架在精确度和召回率方面都取得了成功，并且优于以前的解决方案
英文摘要：
Changes in the structure of observed social and complex networks' structure can indicate a significant underlying change in an organization, or reflect the response of the network to an external event. Automatic detection of change points in evolving networks is rudimentary to the research and the understanding of the effect of such events on networks. Here we present an easy-to-implement and fast framework for change point detection in temporal evolving networks. Unlike previous approaches, our method is size agnostic, and does not require either prior knowledge about the network's size and structure, nor does it require obtaining historical information or nodal identities over time. We use both synthetic data derived from dynamic models and two real datasets: Enron email exchange and Ask-Ubuntu forum. Our framework succeeds with both precision and recall and outperforms previous solutions
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
