Uncertainty quantification in latent position graph models

Music City Center 

From a graph-based perspective, anomaly detection techniques currently deployed in enterprise cyber-security typically act on individual nodes or edges, for example, tracking connectivity patterns of a network host over time or detecting unusual volumes or periodicity in data transfers between two network nodes. Techniques which leverage the full network graph are less common; global network models have typically proved too simplistic in their assumptions, such as the well-studied but arguably overused stochastic block model. A new anomaly detection framework is proposed, which seeks to fully quantify uncertainty in node positions for latent position network graph models. Such a framework admits the possibility for nodes to be identified as outlying through, for example, unusual entropy levels in their perceived graph position rather than simply relying on detecting spatial outliers.

graph embedding