Thumbnail Image
Item

Detecting periodic time scales of changes in temporal networks

Title / Series / Name
Publication Volume
Publication Issue
Pages
Authors
Andres, Elsa
Barrat, Alain
Karsai, Márton
Editors
Keywords
power spectrum
temporal networks
Computer Networks and Communications
Management Science and Operations Research
Control and Optimization
Computational Mathematics
Applied Mathematics
Show all metadata
Files
Thumbnail Image
Karsai-Marton2_2024.pdf
Adobe PDF, 2.24 MB
URI
https://hdl.handle.net/20.500.14018/26471
Abstract
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.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2024-04
Language
ISBN
Identifiers
10.1093/comnet/cnae004
Publisher link
Unit
Collections
Network and Data Science