F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams
This addresses the challenge of anomaly detection in dynamic networks for applications like security or monitoring, though it appears incremental as it builds on existing methods with a novel technique.
The paper tackled the problem of detecting anomalies in edge streams, such as in email or social networks, by proposing F-FADE, which uses frequency factorization to model time-evolving interaction frequencies, achieving state-of-the-art performance in experiments on synthetic and real-world networks.
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.