TY - JOUR
T1 - Detecting periodic time scales of changes in temporal networks
AU - Andres, Elsa
AU - Barrat, Alain
AU - Karsai, Márton
N1 - Publisher Copyright:
© The Author(s) 2023.
PY - 2024/2/21
Y1 - 2024/2/21
N2 - 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 characterizing 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 and temporal event graphs. We define dissimilarity metrics extracted from these representations and compute their power spectra from their Fourier transforms to effectively identify dominant periodic time scales characterizing the changes of the 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 time scales in systems represented by temporal networks.
AB - 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 characterizing 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 and temporal event graphs. We define dissimilarity metrics extracted from these representations and compute their power spectra from their Fourier transforms to effectively identify dominant periodic time scales characterizing the changes of the 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 time scales in systems represented by temporal networks.
KW - power spectrum
KW - temporal networks
UR - http://www.scopus.com/inward/record.url?scp=85185880855&partnerID=8YFLogxK
U2 - 10.1093/comnet/cnae004
DO - 10.1093/comnet/cnae004
M3 - Article
AN - SCOPUS:85185880855
SN - 2051-1310
VL - 12
JO - Journal of Complex Networks
JF - Journal of Complex Networks
IS - 2
M1 - cnae004
ER -