  Anomaly detection is a relevant problem in the area of data analysis. In
networked systems, where individual entities interact in pairs, anomalies are
observed when pattern of interactions deviates from patterns considered
regular. Properly defining what regular patterns entail relies on developing
expressive models for describing the observed interactions. It is crucial to
address anomaly detection in networks. Among the many well-known models for
networks, latent variable models - a class of probabilistic models - offer
promising tools to capture the intrinsic features of the data. In this work, we
propose a probabilistic generative approach that incorporates domain knowledge,
i.e., community membership, as a fundamental model for regular behavior, and
thus flags potential anomalies deviating from this pattern. In fact, community
membership serves as the building block of a null model to identify the regular
interaction patterns. The structural information is included in the model
through latent variables for community membership and anomaly parameter. The
algorithm aims at inferring these latent parameters and then output the labels
identifying anomalies on the network edges.
