A shrinkage approach to tracking dynamic networks

The analysis of network data is of interest to many disciplines, ranging from sociology to computer science. Recent interest has shifted from static networks to dynamic networks, which evolve over time. A fundamental problem in the analysis of dynamic networks is tracking long-term trends, which are obscured by short-term variations. In this paper, we propose a method for minimum mean-squared error tracking of dynamic networks using a recursive shrinkage estimation framework that accounts for the spatial correlation in the network. Unlike model-based tracking methods such as the Kalman filter, the proposed method does not require knowledge about the network dynamics. We demonstrate that the proposed method is able to track dynamic networks effectively through experiments on simulated and real networks.