  Many temporal networks exhibit multiple system states, such as weekday and
weekend patterns in social contact networks. The detection of such distinct
states in temporal network data has recently been explored as it helps reveal
underlying dynamical processes. A commonly used method is network aggregation
over a time window, which aggregates a subsequence of multiple network
snapshots into one static network. This method, however, necessarily discards
temporal dynamics within the time window. Here we develop a new method for
detecting dynamic states in temporal networks using information regarding the
timeline of contacts between each pair of nodes. We apply a similarity measure
informed by the techniques of processing time series and community detection to
sequentially discompose a given temporal network into multiple dynamic states
(including repeated ones). Experiments with empirical temporal network data
demonstrated that our method outperformed the conventional approach using
simple network aggregation in revealing interpretable system states. In
addition, our method allows users to analyze hierarchical temporal structures
and to uncover dynamic state at different spatial/temporal resolutions.
