Title: Change Detection in a Dynamic Stream of Attributed Networks,
Abstract: While anomaly detection in static networks has been extensively studied, only
recently, researchers have focused on dynamic networks. This trend is mainly
due to the capacity of dynamic networks in representing complex physical,
biological, cyber, and social systems. This paper proposes a new methodology
for modeling and monitoring of dynamic attributed networks for quick detection
of temporal changes in network structures. In this methodology, the generalized
linear model (GLM) is used to model static attributed networks. This model is
then combined with a state transition equation to capture the dynamic behavior
of the system. Extended Kalman filter (EKF) is used as an online, recursive
inference procedure to predict and update network parameters over time. In
order to detect changes in the underlying mechanism of edge formation,
prediction residuals are monitored through an Exponentially Weighted Moving
Average (EWMA) control chart. The proposed modeling and monitoring procedure is
examined through simulations for attributed binary and weighted networks. The
email communication data from the Enron corporation is used as a case study to
show how the method can be applied in real-world problems.