Flow stability for dynamic community detection

Flow stability for dynamic community detection. Bovet, A., Delvenne, J., & Lambiotte, R. Science Advances, 8(19):eabj3063, May, 2022.

Paper doi abstract bibtex

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. , The flow stability method extracts simplified descriptions of complex time-resolved datasets at different dynamical scales.

@article{bovetFlowStabilityDynamic2022,
	title = {Flow stability for dynamic community detection},
	volume = {8},
	copyright = {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA)},
	issn = {2375-2548},
	url = {https://www.science.org/doi/10.1126/sciadv.abj3063},
	doi = {10.1126/sciadv.abj3063},
	abstract = {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.
          , 
            The flow stability method extracts simplified descriptions of complex time-resolved datasets at different dynamical scales.},
	language = {en},
	number = {19},
	urldate = {2022-05-12},
	journal = {Science Advances},
	author = {Bovet, Alexandre and Delvenne, Jean-Charles and Lambiotte, Renaud},
	month = may,
	year = {2022},
	keywords = {community detection, diffusion, network science, temporal networks},
	pages = {eabj3063},
}

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