Anomaly detection and community detection in networks


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 which incorporates domain knowledge, i.e., community membership, as a fundamental model for regular behavior, and thus flag potential anomalies deviating from this pattern. In fact, community membership act as the building blocks 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.

Accepted at Journal of Big Data
Hadiseh Safdari
Hadiseh Safdari
Postdoctoral researcher

My current research revolves around inference and modeling in networks. More precisely, we aim to relax the independence assumptions in generative models by deploying hidden variables, and establishing analytical approximations to make the inference problem tractable.

Caterina De Bacco
Caterina De Bacco
CyberValley Research Group Leader

My research focuses on understanding, optimizing and predicting relations between the microscopic and macroscopic properties of complex large-scale interacting systems.