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Article Dans Une Revue Journal of Complex Networks Année : 2024

Detecting periodic time scales of changes in temporal networks

Résumé

Temporal networks are commonly used to represent dynamical complex systems like social networks, simultaneous firing of neurons, human mobility or public transportation. Their dynamics may evolve on multiple time scales characterising for instance periodic activity patterns or structural changes. The detection of these time scales can be challenging from the direct observation of simple dynamical network properties like the activity of nodes or the density of links. Here we propose two new methods, which rely on already established static representations of temporal networks, namely supra-adjacency matrices and temporal event graphs. We define dissimilarity metrics extracted from these representations and compute their Fourier Transform to effectively identify dominant periodic time scales characterising the original temporal network. We demonstrate our methods using synthetic and real-world data sets describing various kinds of temporal networks. We find that while in all cases the two methods outperform the reference measures, the supra-adjacency based method identifies more easily periodic changes in network density, while the temporal event graph based method is better suited to detect periodic changes in the group structure of the network. Our methodology may provide insights into different phenomena occurring at multiple timescales in systems represented by temporal networks.
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Origine : Publication financée par une institution
Licence : CC BY - Paternité

Dates et versions

hal-04476792 , version 1 (25-02-2024)

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Paternité

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Elsa Andres, Alain Barrat, Márton Karsai. Detecting periodic time scales of changes in temporal networks. Journal of Complex Networks, 2024, 12 (2), pp.cnae004. ⟨10.1093/comnet/cnae004⟩. ⟨hal-04476792⟩
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