  Network data has emerged as an active research area in statistics. Much of
the focus of ongoing research has been on static networks that represent a
single snapshot or aggregated historical data unchanging over time. However,
most networks result from temporally-evolving systems that exhibit intrinsic
dynamic behavior. Monitoring such temporally-varying networks to detect
anomalous changes has applications in both social and physical sciences. In
this work, we perform an evaluation study of summary statistics for anomaly
detection in temporally-evolving networks by incorporating principles from
statistical process monitoring. In contrast to previous studies, we
deliberately incorporate temporal auto-correlation in our study. Other
considerations in our comprehensive assessment include types and duration of
anomaly, model type, and sparsity in temporally-evolving networks. We conclude
that summary statistics can be valuable tools for network monitoring and often
perform better than more complicated statistics.
