  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.
