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Many temporal networks exhibit multiple system states, such as weekday andweekend patterns in social contact networks. The detection of such distinctstates in temporal network data has recently been explored as it helps revealunderlying dynamical processes. A commonly used method is network aggregationover a time window, which aggregates a subsequence of multiple networksnapshots into one static network. This method, however, necessarily discardstemporal dynamics within the time window. Here we develop a new method fordetecting dynamic states in temporal networks using information regarding thetimeline of contacts between each pair of nodes. We apply a similarity measureinformed by the techniques of processing time series and community detection tosequentially discompose a given temporal network into multiple dynamic states(including repeated ones). Experiments with empirical temporal network datademonstrated that our method outperformed the conventional approach usingsimple network aggregation in revealing interpretable system states. Inaddition, our method allows users to analyze hierarchical temporal structuresand to uncover dynamic state at different spatial/temporal resolutions.

Detecting Dynamic States of Temporal Networks Using Connection Series Tensors