Before revision:  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 .
After revision:  Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, 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 effectively .