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Flow stability for dynamic community detection
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DOI：10.1126/sciadv.abj3063.png)
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摘要
En 中文
Many systems exhibit complex temporal dynamics due to the presence of different processes taking place simultaneously. An important task in these systems is to extract a simplified view of their time-dependent network of interactions. Community detection in temporal networks usually relies on aggregation over time windows or consider sequences of different stationary epochs. For dynamics-based methods, attempts to generalize static-network methodologies also face the fundamental difficulty that a stationary state of the dynamics does not always exist. Here, we derive a method based on a dynamical process evolving on the temporal network. Our method allows dynamics that do not reach a steady state and uncovers two sets of communities for a given time interval that accounts for the ordering of edges in forward and backward time. We show that our method provides a natural way to disentangle the different dynamical scales present in a system with synthetic and real-world examples.
Keyword:
RANDOM-WALKS
MULTISCALE
DISCOVERY
NETWORKS
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期刊
IF:
12.5
论文数:
2.1W
被引数:
18.1W
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