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 studied as it helps reveal underlying dynamical processes. A commonly used method is network aggregation over a certain 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 propose a new method for detecting dynamic states in temporal networks using connection series (i.e., time series of connection status) between nodes. Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, and community detection in the similarity network of those time windows . Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states effectively . In addition, our method allows users to analyze hierarchical temporal structures and to uncover dynamic states at different spatial/temporal resolutions.
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.
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connection series (i.e., time series of connection status) between nodes. Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors,
information regarding the timeline of contacts between each pair of nodes. We apply a similarity measure informed by the techniques of processing time series
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in the similarity network of those time windows
to sequentially discompose a given temporal network into multiple dynamic states (including repeated ones)
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effectively
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