Detecting node propensity changes in the dynamic degree corrected stochastic block model

Many applications involve dynamic networks for which a sequence of snapshots of network structure is available over time. Studying the evolution of node propensity over time can be important in exploring and analyzing these networks. In this paper, we propose a multivariate surveillance plan to monitor node propensity in the dynamic degree corrected stochastic block model. The method is flexible enough to detect anomalous nodes that arise from different mechanisms, including individual change, individuals switch, and global change. Experiments on simulated and case study social network data streams demonstrate that our surveillance strategy can efficiently detect node propensity changes in dynamic networks.