Anomaly detection and community detection in networks

TL;DR

This paper introduces a probabilistic generative model that leverages community membership as a baseline to detect anomalies in network interactions, improving understanding of irregular patterns.

Contribution

It proposes a novel latent variable model incorporating community information for anomaly detection in networks, enhancing existing probabilistic approaches.

Findings

Effective identification of anomalous edges in networks.

Incorporation of community structure improves detection accuracy.

Model demonstrates robustness across different network datasets.

Abstract

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.