Seasonal Stochastic Blockmodeling for Anomaly Detection in Dynamic Networks

Sociotechnological and geospatial processes exhibit time varying structure that make insight discovery challenging. To detect abnormal moments in these processes, a de€nition of ‘normal’ must be established. Œis paper proposes a new statistical model for such systems, modeled as dynamic networks, to address this challenge. It assumes that vertices fall into one of k types and that the probability of edge formation at a particular time depends on the types of the incident nodes and the current time. Œe time dependencies are driven by unique seasonal processes, which many systems exhibit (e.g., predictable spikes in geospatial or web trac each day). Œe paper de€nes the model as a generative process and an inference procedure to recover the ‘normal’ seasonal processes from data when they are unknown. An outline of anomaly detection experiments to be completed over Enron emails and New York City taxi trips is presented.