F-FADE：边缘流异常检测的频率分解
F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams
摘要：
边缘流通常用于捕获动态网络中的交互，例如电子邮件、社交或计算机网络。边缘流中异常或罕见事件的检测问题有着广泛的应用。然而，由于缺乏标签，交互的高度动态性以及网络中时间和结构变化的纠缠，它带来了许多挑战。目前的方法在解决上述挑战和有效处理大量相互作用的能力方面是有限的。在这里，我们提出了F-FADE，边缘流中的异常检测的一种新方法，它使用了一种新的频率因子分解技术，有效地模拟节点对之间的相互作用的频率的时间演化分布。然后，基于观察到的每个传入交互的频率的可能性来确定异常。F-FADE能够在在线流媒体设置中处理具有时间和结构变化的各种异常，同时仅需要恒定的内存。我们在一个合成网络和六个真实动态网络上的实验表明，F-FADE实现了最先进的性能，并且可以检测到以前的方法无法发现的异常。
英文摘要：
Edge streams are commonly used to capture interactions in dynamic networks, such as email, social, or computer networks. The problem of detecting anomalies or rare events in edge streams has a wide range of applications. However, it presents many challenges due to lack of labels, a highly dynamic nature of interactions, and the entanglement of temporal and structural changes in the network. Current methods are limited in their ability to address the above challenges and to efficiently process a large number of interactions. Here, we propose F-FADE, a new approach for detection of anomalies in edge streams, which uses a novel frequency-factorization technique to efficiently model the time-evolving distributions of frequencies of interactions between node-pairs. The anomalies are then determined based on the likelihood of the observed frequency of each incoming interaction. F-FADE is able to handle in an online streaming setting a broad variety of anomalies with temporal and structural changes, while requiring only constant memory. Our experiments on one synthetic and six real-world dynamic networks show that F-FADE achieves state of the art performance and may detect anomalies that previous methods are unable to find.
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。
