Introduction Disease surveillance data often have an underlying network structure (e.g., for outbreaks that spread by person-to-person contact). If the underlying graph structure is known, detection methods such as GraphScan (1) can be used to identify an anomalous subgraph, indicative of an emerging event. Typically, however, the network structure is unknown and must be learned from unlabeled data, given only the time series of observed counts (e.g., daily hospital visits for each zip code).