Multiple network embedding for anomaly detection in time series of graphs

The problem of anomaly detection in time series of graphs is considered, focusing on two related inference tasks: the detection of anomalous graphs within a time series and the detection of temporally anomalous vertices. These tasks are approached via the adaptation of multiple adjacency spectral embedding (MASE), a statistically principled method for joint graph inference. The effectiveness of the method is demonstrated for these inference tasks, and its performance is assessed based on the nature of detectable anomalies. Theoretical justification is provided, along with insights into its use. The approach identifies anomalous vertices beyond just large degree changes when applied to the Enron communication graph, a large-scale commercial search engine time series, and a larval Drosophila connectome.