Anomaly detection in autoregressive networks
We study anomaly detection in temporally dependent network sequences. Methods based only on adjacency matrices, which are widely used for static networks, can miss changes in the way edges evolve. We instead represent each pair of consecutive networks by separate formation and dissolution event matrices, and embed the...