Abstract
Scan statistics are commonly used to investigate an instantiation of a random field for the possible presence of a local signal. Known in the engineering literature as “moving window analysis”, the idea is to scan a small window over the data, calculating some local statistic (number of events for a point pattern, perhaps, or average pixel value for an image) for each window. The supremum or maximum of these locality statistics is known as the scan statistic. In this talk, we introduce a theory of scan statistics on graphs, and we apply these ideas to the problem of anomaly detection in a time series of Enron email Graphs.